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[ { "type": "text", "value": "๐Ÿš€ Call for all AI innovators in the United Arab Emirates! ", "raw": "๐Ÿš€ Call for all AI innovators in the United Arab Emirates! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Meta and Startupbootcamp MENA is launching the Llama Design Drive a one of its kind AI Accelerator in collaboration with the Roads and Transport Authority, Emirates, Dubai Holding and Chalhoub Group. ", "raw": "Meta and Startupbootcamp MENA is launching the Llama Design Drive a one of its kind AI Accelerator in collaboration with the Roads and Transport Authority, Emirates, Dubai Holding and Chalhoub Group. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "We are calling on all AI startups, developers, agencies and university labs to apply to this program for a chance to solve 1 out of 7 business challenges endorsed by our program partners using Llama 3.1, Meta's open source Large Language Model.", "raw": "We are calling on all AI startups, developers, agencies and university labs to apply to this program for a chance to solve 1 out of 7 business challenges endorsed by our program partners using Llama 3.1, Meta's open source Large Language Model.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Startups selected into the program will attend a 4-week product design sprint at Dubai Future Foundation powered by Startupbootcamp with support from Meta engineering teams. Selected startups will also have the chance to be fast-tracked into a global program sponsored by Meta, with the chance of receiving up to $500,000 to further support the development of their AI products.", "raw": "Startups selected into the program will attend a 4-week product design sprint at Dubai Future Foundation powered by Startupbootcamp with support from Meta engineering teams. Selected startups will also have the chance to be fast-tracked into a global program sponsored by Meta, with the chance of receiving up to $500,000 to further support the development of their AI products.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Application deadline is August 31 so be sure to apply fast!", "raw": "Application deadline is August 31 so be sure to apply fast!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "For more information and to apply: ", "raw": "For more information and to apply: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://llamadesigndrive.com", "href": "https://llamadesigndrive.com", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
๐Ÿš€ Call for all AI innovators in the United Arab Emirates! Meta and Startupbootcamp MENA is launching the Llama Design Drive a one of its kind AI Accelerator in collaboration with the Roads and Transport Authority, Emirates, Dubai Holding and Chalhoub Group. We are calling on all AI startups, developers, agencies and university labs to apply to this program for a chance to solve 1 out of 7 business challenges endorsed by our program partners using Llama 3.1, Meta's open source Large Language Model. Startups selected into the program will attend a 4-week product design sprint at Dubai Future Foundation powered by Startupbootcamp with support from Meta engineering teams. Selected startups will also have the chance to be fast-tracked into a global program sponsored by Meta, with the chance of receiving up to $500,000 to further support the development of their AI products. Application deadline is August 31 so be sure to apply fast! For more information and to apply: https://llamadesigndrive.com
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2024-08-03T08:58:38.000Z
2024-08-03T15:31:20.718Z
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/posts/hamdanuk/381069767685423
602
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414396915473226
[ { "type": "text", "value": "๐Ÿ™‹๐Ÿปโ€โ™‚๏ธHey there folks ,", "raw": "๐Ÿ™‹๐Ÿปโ€โ™‚๏ธHey there folks ,", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I found this cool (new?) thing by Docker called Testcontainers , and there's an ", "raw": "I found this cool (new?) thing by Docker called Testcontainers , and there's an ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@ollama", "href": null, "resource": null, "url": null, "code": null, "user": "ollama", "label": null, "lang": null }, { "type": "text", "value": " object that you can use to programmatically serve ephemeral containers and LLMs.", "raw": " object that you can use to programmatically serve ephemeral containers and LLMs.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I made a post about it here : ", "raw": "I made a post about it here : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/Tonic/localai-testcontainers", "href": "https://huggingface.co/blog/Tonic/localai-testcontainers", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It's really useful, powerful and fun !", "raw": "It's really useful, powerful and fun !", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Demo coming soon ๐Ÿค—", "raw": "Demo coming soon ๐Ÿค—", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
๐Ÿ™‹๐Ÿปโ€โ™‚๏ธHey there folks , I found this cool (new?) thing by Docker called Testcontainers , and there's an @ollama object that you can use to programmatically serve ephemeral containers and LLMs. I made a post about it here : https://huggingface.co/blog/Tonic/localai-testcontainers It's really useful, powerful and fun ! Demo coming soon ๐Ÿค—
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[]
2024-08-03T07:42:07.000Z
2024-08-03T07:42:07.333Z
[]
/posts/Tonic/414396915473226
734
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564304258433170
[ { "type": "text", "value": "Black Forest Labs, BASED! ๐Ÿ‘", "raw": "Black Forest Labs, BASED! ๐Ÿ‘", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "FLUX.1 is more delightful, with good instruction following. ", "raw": "FLUX.1 is more delightful, with good instruction following. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "FLUX.1 dev(", "raw": "FLUX.1 dev(", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/black-forest-labs/FLUX.1-dev", "href": null, "resource": { "type": "model", "id": "black-forest-labs/FLUX.1-dev", "discussionNum": null }, "url": "https://huggingface.co/black-forest-labs/FLUX.1-dev", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ") with a 12B parameter distillation model, second only to Black Forest Labs' state-of-the-art model FLUX.1 pro. ๐Ÿ™€", "raw": ") with a 12B parameter distillation model, second only to Black Forest Labs' state-of-the-art model FLUX.1 pro. ๐Ÿ™€", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Update ๐Ÿค™Official demo: ", "raw": "Update ๐Ÿค™Official demo: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/black-forest-labs/FLUX.1-dev", "href": null, "resource": { "type": "space", "id": "black-forest-labs/FLUX.1-dev", "discussionNum": null }, "url": "https://huggingface.co/spaces/black-forest-labs/FLUX.1-dev", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Black Forest Labs, BASED! ๐Ÿ‘ FLUX.1 is more delightful, with good instruction following. FLUX.1 dev(https://huggingface.co/black-forest-labs/FLUX.1-dev) with a 12B parameter distillation model, second only to Black Forest Labs' state-of-the-art model FLUX.1 pro. ๐Ÿ™€ Update ๐Ÿค™Official demo: https://huggingface.co/spaces/black-forest-labs/FLUX.1-dev
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2024-08-02T18:30:30.000Z
2024-08-03T07:44:34.757Z
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/posts/vilarin/564304258433170
4,187
1
460826571056288
[ { "type": "text", "value": "Just dropped magpie-ultra-v0.1! The first open synthetic dataset generated with Llama 3.1 405B. Created with distilabel, it's our most advanced and compute-intensive pipeline to date. We made the GPUs of the cluster go brrrrr ๐Ÿš€", "raw": "Just dropped magpie-ultra-v0.1! The first open synthetic dataset generated with Llama 3.1 405B. Created with distilabel, it's our most advanced and compute-intensive pipeline to date. We made the GPUs of the cluster go brrrrr ๐Ÿš€", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/argilla/magpie-ultra-v0.1", "href": null, "resource": { "type": "dataset", "id": "argilla/magpie-ultra-v0.1", "discussionNum": null }, "url": "https://huggingface.co/datasets/argilla/magpie-ultra-v0.1", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Take it a look and tell us what you think! Probably, the models taking the most out of it are smol models ๐Ÿค— We will be improving the dataset in upcoming iterations!", "raw": "Take it a look and tell us what you think! Probably, the models taking the most out of it are smol models ๐Ÿค— We will be improving the dataset in upcoming iterations!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Just dropped magpie-ultra-v0.1! The first open synthetic dataset generated with Llama 3.1 405B. Created with distilabel, it's our most advanced and compute-intensive pipeline to date. We made the GPUs of the cluster go brrrrr ๐Ÿš€ https://huggingface.co/datasets/argilla/magpie-ultra-v0.1 Take it a look and tell us what you think! Probably, the models taking the most out of it are smol models ๐Ÿค— We will be improving the dataset in upcoming iterations!
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2024-08-02T15:58:33.000Z
2024-08-02T15:59:03.532Z
[]
/posts/gabrielmbmb/460826571056288
3,531
0
524871051582642
[ { "type": "text", "value": "Looks like Black Forest Labs is taking the Hub by storm with its text-to-image models, holding 3 of the top 5 positions on the Trending dashboard", "raw": "Looks like Black Forest Labs is taking the Hub by storm with its text-to-image models, holding 3 of the top 5 positions on the Trending dashboard", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/black-forest-labs", "href": "https://huggingface.co/black-forest-labs", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Have you tried them yet?", "raw": "Have you tried them yet?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Looks like Black Forest Labs is taking the Hub by storm with its text-to-image models, holding 3 of the top 5 positions on the Trending dashboard https://huggingface.co/black-forest-labs Have you tried them yet?
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2024-08-02T15:24:47.000Z
2024-08-02T15:25:06.494Z
[]
/posts/fdaudens/524871051582642
578
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OWLSAM2: text-promptable SAM2 ๐Ÿฆ‰ https://huggingface.co/spaces/merve/OWLSAM2 Marrying cutting-edge zero-shot object detector OWLv2 ๐Ÿค mask generator SAM2 (small checkpoint) Zero-shot segmentation with insane precision โ›ต๏ธ I also uploaded all models with usage snippets and made a collection of SAM2 models and demos https://huggingface.co/collections/merve/sam2-66ac9deac6fca3bc5482fe30
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2024-08-02T10:09:37.000Z
2024-08-03T10:22:33.948Z
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[ { "type": "text", "value": "FLUX FP16 produces better quality than FP8 but requires 28 GB VRAM - Full comparisons - Also compared Dev vs Turbo model and 1024 vs 1536", "raw": "FLUX FP16 produces better quality than FP8 but requires 28 GB VRAM - Full comparisons - Also compared Dev vs Turbo model and 1024 vs 1536", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "check the file names in the below given imgsli to see all details", "raw": "check the file names in the below given imgsli to see all details", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "SwarmUI on L40S is used to compare - 1.82 it / second step speed for 1024x1024", "raw": "SwarmUI on L40S is used to compare - 1.82 it / second step speed for 1024x1024", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "imgsli link that compares all : ", "raw": "imgsli link that compares all : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://imgsli.com/MjgzNzM1", "href": "https://imgsli.com/MjgzNzM1", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "SwarmUI full tutorial public post : ", "raw": "SwarmUI full tutorial public post : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.patreon.com/posts/106135985", "href": "https://www.patreon.com/posts/106135985", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "1-Click FLUX models downloader scripts for Windows, RunPod and Massed Compute are in below post", "raw": "1-Click FLUX models downloader scripts for Windows, RunPod and Massed Compute are in below post", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.patreon.com/posts/109289967", "href": "https://www.patreon.com/posts/109289967", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "free Kaggle account notebook that supports FLUX already : Download from here : ", "raw": "free Kaggle account notebook that supports FLUX already : Download from here : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.patreon.com/posts/106650931", "href": "https://www.patreon.com/posts/106650931", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "prompt :", "raw": "prompt :", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "(medium full shot) of (awe-inspiring snake) with muscular body, amber eyes, bronze brown armored scales, venomous fangs, coiling tail, gemstone-studded scales frills, set in a barren desert wasteland, with cracked earth and the remains of ancient structures, a place of mystery and danger, at dawn, ,Masterpiece,best quality, raw photo, realistic, very aesthetic, dark", "raw": "(medium full shot) of (awe-inspiring snake) with muscular body, amber eyes, bronze brown armored scales, venomous fangs, coiling tail, gemstone-studded scales frills, set in a barren desert wasteland, with cracked earth and the remains of ancient structures, a place of mystery and danger, at dawn, ,Masterpiece,best quality, raw photo, realistic, very aesthetic, dark", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", 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"type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Zero to Hero Stable Diffusion 3 Tutorial with Amazing SwarmUI SD Web UI that Utilizes ComfyUI", "raw": "Zero to Hero Stable Diffusion 3 Tutorial with Amazing SwarmUI SD Web UI that Utilizes ComfyUI", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://youtu.be/HKX8_F1Er_w", "href": "https://youtu.be/HKX8_F1Er_w", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { 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3 on Cloud Services Kaggle (free), Massed Compute & RunPod", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://youtu.be/XFUZof6Skkw", "href": "https://youtu.be/XFUZof6Skkw", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
FLUX FP16 produces better quality than FP8 but requires 28 GB VRAM - Full comparisons - Also compared Dev vs Turbo model and 1024 vs 1536 check the file names in the below given imgsli to see all details SwarmUI on L40S is used to compare - 1.82 it / second step speed for 1024x1024 imgsli link that compares all : https://imgsli.com/MjgzNzM1 SwarmUI full tutorial public post : https://www.patreon.com/posts/106135985 1-Click FLUX models downloader scripts for Windows, RunPod and Massed Compute are in below post https://www.patreon.com/posts/109289967 free Kaggle account notebook that supports FLUX already : Download from here : https://www.patreon.com/posts/106650931 prompt : (medium full shot) of (awe-inspiring snake) with muscular body, amber eyes, bronze brown armored scales, venomous fangs, coiling tail, gemstone-studded scales frills, set in a barren desert wasteland, with cracked earth and the remains of ancient structures, a place of mystery and danger, at dawn, ,Masterpiece,best quality, raw photo, realistic, very aesthetic, dark CFG 1 - seed 1 - FLUX CFG is default : 3.5 Full public SwarmUI tutorial Zero to Hero Stable Diffusion 3 Tutorial with Amazing SwarmUI SD Web UI that Utilizes ComfyUI https://youtu.be/HKX8_F1Er_w Full public Cloud SwarmUI tutorial How to Use SwarmUI & Stable Diffusion 3 on Cloud Services Kaggle (free), Massed Compute & RunPod https://youtu.be/XFUZof6Skkw
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[]
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2024-08-02T01:00:48.000Z
2024-08-02T01:00:48.832Z
[]
/posts/MonsterMMORPG/346090432958186
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[ { "type": "text", "value": "Hugging Face famous organisations activity. Guess which one has the word \"Open\" in it ๐Ÿ˜‚", "raw": "Hugging Face famous organisations activity. Guess which one has the word \"Open\" in it ๐Ÿ˜‚", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Hugging Face famous organisations activity. Guess which one has the word "Open" in it ๐Ÿ˜‚
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2024-08-01T13:00:33.000Z
2024-08-02T14:52:06.929Z
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/posts/victor/585253216448265
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[ { "type": "text", "value": "๐—ฆ๐—”๐—  ๐Ÿฎ ๐—ฟ๐—ฒ๐—น๐—ฒ๐—ฎ๐˜€๐—ฒ๐—ฑ: ๐—ก๐—ฒ๐˜„ ๐—ฆ๐—ข๐—ง๐—” ๐—ผ๐—ป ๐˜€๐—ฒ๐—ด๐—บ๐—ฒ๐—ป๐˜๐—ฎ๐˜๐—ถ๐—ผ๐—ป, ๐—ฏ๐˜† ๐—ฐ๐—ผ๐—บ๐—ฏ๐—ถ๐—ป๐—ถ๐—ป๐—ด ๐˜€๐˜†๐—ป๐˜๐—ต๐—ฒ๐˜๐—ถ๐—ฐ ๐—ฑ๐—ฎ๐˜๐—ฎ ๐˜„๐—ถ๐˜๐—ต ๐—ต๐˜‚๐—บ๐—ฎ๐—ป ๐—ณ๐—ฒ๐—ฒ๐—ฑ๐—ฏ๐—ฎ๐—ฐ๐—ธ ๐Ÿš€", "raw": "๐—ฆ๐—”๐—  ๐Ÿฎ ๐—ฟ๐—ฒ๐—น๐—ฒ๐—ฎ๐˜€๐—ฒ๐—ฑ: ๐—ก๐—ฒ๐˜„ ๐—ฆ๐—ข๐—ง๐—” ๐—ผ๐—ป ๐˜€๐—ฒ๐—ด๐—บ๐—ฒ๐—ป๐˜๐—ฎ๐˜๐—ถ๐—ผ๐—ป, ๐—ฏ๐˜† ๐—ฐ๐—ผ๐—บ๐—ฏ๐—ถ๐—ป๐—ถ๐—ป๐—ด ๐˜€๐˜†๐—ป๐˜๐—ต๐—ฒ๐˜๐—ถ๐—ฐ ๐—ฑ๐—ฎ๐˜๐—ฎ ๐˜„๐—ถ๐˜๐—ต ๐—ต๐˜‚๐—บ๐—ฎ๐—ป ๐—ณ๐—ฒ๐—ฒ๐—ฑ๐—ฏ๐—ฎ๐—ฐ๐—ธ ๐Ÿš€", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It's a model for Object segmentation, for both image and video:", "raw": "It's a model for Object segmentation, for both image and video:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ‘‰ input = a text prompt, or a click on a specific object", "raw": "๐Ÿ‘‰ input = a text prompt, or a click on a specific object", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ‘‰ output = the model draws a mask around the object. In video segmentation, the mask should follow the object's movements (it is then called a masklet)", "raw": "๐Ÿ‘‰ output = the model draws a mask around the object. In video segmentation, the mask should follow the object's movements (it is then called a masklet)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ’ช SAM 2 is 6x faster than the previous version, it now also works on a video, and it beats SOTA by far on both image and video segmentation tasks.", "raw": "๐Ÿ’ช SAM 2 is 6x faster than the previous version, it now also works on a video, and it beats SOTA by far on both image and video segmentation tasks.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "How did they pull that?", "raw": "How did they pull that?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The main blocker for video segmentation was that data is really hard to collect: to build your training dataset, should you manually draw masks on every frame? That would be way too costly! โžก๏ธ As a result, existing video segmentation datasets have a real lack of coverage: few examples, few masklets drawn.", "raw": "The main blocker for video segmentation was that data is really hard to collect: to build your training dataset, should you manually draw masks on every frame? That would be way too costly! โžก๏ธ As a result, existing video segmentation datasets have a real lack of coverage: few examples, few masklets drawn.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ’ก Key idea: researchers they decided to use a segmentation model to help them collect the dataset.", "raw": "๐Ÿ’ก Key idea: researchers they decided to use a segmentation model to help them collect the dataset.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "But then itโ€™s a chicken and egg problem: you need the model to create the dataset and the opposite as well? ๐Ÿค”", "raw": "But then itโ€™s a chicken and egg problem: you need the model to create the dataset and the opposite as well? ๐Ÿค”", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โ‡’ To solve this, they build a data generation system that they scale up progressively in 3 successive manual annotations phases:", "raw": "โ‡’ To solve this, they build a data generation system that they scale up progressively in 3 successive manual annotations phases:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐—ฆ๐˜๐—ฒ๐—ฝ ๐Ÿญ: Annotators use only SAM + manual editing tools on each frame โ‡’ Create 16k masklets across 1.4k videos", "raw": "๐—ฆ๐˜๐—ฒ๐—ฝ ๐Ÿญ: Annotators use only SAM + manual editing tools on each frame โ‡’ Create 16k masklets across 1.4k videos", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐—ฆ๐˜๐—ฒ๐—ฝ ๐Ÿฎ: Then train a first SAM 2, add it in the loop to temporally propagate frames, and correct by re-doing a mask manually when an error has occured โ‡’ This gets a 5.1x speedup over data collection in phase 1! ๐Ÿƒ Collect 60k masklets", "raw": "๐—ฆ๐˜๐—ฒ๐—ฝ ๐Ÿฎ: Then train a first SAM 2, add it in the loop to temporally propagate frames, and correct by re-doing a mask manually when an error has occured โ‡’ This gets a 5.1x speedup over data collection in phase 1! ๐Ÿƒ Collect 60k masklets", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐—ฆ๐˜๐—ฒ๐—ฝ ๐Ÿฏ: Now SAM 2 is more powerful, it has the โ€œsingle clickโ€ prompting option, thus annotators can use it with simple clicks to re-annotate data.", "raw": "๐—ฆ๐˜๐—ฒ๐—ฝ ๐Ÿฏ: Now SAM 2 is more powerful, it has the โ€œsingle clickโ€ prompting option, thus annotators can use it with simple clicks to re-annotate data.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "They even add a completely automatic step to generate 350k more masklets!", "raw": "They even add a completely automatic step to generate 350k more masklets!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "And in turn, the model perf gradually increases.", "raw": "And in turn, the model perf gradually increases.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I find this a great example of combining synthetic data generation with human annotation ๐Ÿ‘", "raw": "I find this a great example of combining synthetic data generation with human annotation ๐Ÿ‘", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
๐—ฆ๐—”๐—  ๐Ÿฎ ๐—ฟ๐—ฒ๐—น๐—ฒ๐—ฎ๐˜€๐—ฒ๐—ฑ: ๐—ก๐—ฒ๐˜„ ๐—ฆ๐—ข๐—ง๐—” ๐—ผ๐—ป ๐˜€๐—ฒ๐—ด๐—บ๐—ฒ๐—ป๐˜๐—ฎ๐˜๐—ถ๐—ผ๐—ป, ๐—ฏ๐˜† ๐—ฐ๐—ผ๐—บ๐—ฏ๐—ถ๐—ป๐—ถ๐—ป๐—ด ๐˜€๐˜†๐—ป๐˜๐—ต๐—ฒ๐˜๐—ถ๐—ฐ ๐—ฑ๐—ฎ๐˜๐—ฎ ๐˜„๐—ถ๐˜๐—ต ๐—ต๐˜‚๐—บ๐—ฎ๐—ป ๐—ณ๐—ฒ๐—ฒ๐—ฑ๐—ฏ๐—ฎ๐—ฐ๐—ธ ๐Ÿš€ It's a model for Object segmentation, for both image and video: ๐Ÿ‘‰ input = a text prompt, or a click on a specific object ๐Ÿ‘‰ output = the model draws a mask around the object. In video segmentation, the mask should follow the object's movements (it is then called a masklet) ๐Ÿ’ช SAM 2 is 6x faster than the previous version, it now also works on a video, and it beats SOTA by far on both image and video segmentation tasks. How did they pull that? The main blocker for video segmentation was that data is really hard to collect: to build your training dataset, should you manually draw masks on every frame? That would be way too costly! โžก๏ธ As a result, existing video segmentation datasets have a real lack of coverage: few examples, few masklets drawn. ๐Ÿ’ก Key idea: researchers they decided to use a segmentation model to help them collect the dataset. But then itโ€™s a chicken and egg problem: you need the model to create the dataset and the opposite as well? ๐Ÿค” โ‡’ To solve this, they build a data generation system that they scale up progressively in 3 successive manual annotations phases: ๐—ฆ๐˜๐—ฒ๐—ฝ ๐Ÿญ: Annotators use only SAM + manual editing tools on each frame โ‡’ Create 16k masklets across 1.4k videos ๐—ฆ๐˜๐—ฒ๐—ฝ ๐Ÿฎ: Then train a first SAM 2, add it in the loop to temporally propagate frames, and correct by re-doing a mask manually when an error has occured โ‡’ This gets a 5.1x speedup over data collection in phase 1! ๐Ÿƒ Collect 60k masklets ๐—ฆ๐˜๐—ฒ๐—ฝ ๐Ÿฏ: Now SAM 2 is more powerful, it has the โ€œsingle clickโ€ prompting option, thus annotators can use it with simple clicks to re-annotate data. They even add a completely automatic step to generate 350k more masklets! And in turn, the model perf gradually increases. I find this a great example of combining synthetic data generation with human annotation ๐Ÿ‘
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2024-08-01T12:40:18.000Z
2024-08-01T12:40:18.070Z
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Running Gemma 2 2B at 41.66 tokens/s on my MacBook ๐Ÿ’ป๐Ÿš€ - MLX Community's swift conversion - One-line download from the Hub - Small yet powerful on-device model Try it yourself: https://huggingface.co/collections/mlx-community/google-gemma2-667dca89bc9abbfa34080066 #GemmaAI #OnDeviceAI #MachineLearning
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2024-07-31T23:59:02.000Z
2024-08-02T08:09:38.868Z
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nice
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2024-07-31T19:57:28.000Z
2024-07-31T19:57:28.851Z
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Free LLM/RAG course at https://mltblog.com/48GebAG - learn how to build custom architectures from scratch, and earn an LLM certification, all free. The GenAItechLab Fellowship program allows participants to work on state-of-the-art, enterprise-grade projects, entirely for free, at their own pace, at home or in their workplace. The goal is to help you test, enhance, and further implement applications that outperform solutions offered by AI startups or organizations such as Google or OpenAI. You will learn how to quickly build faster and lighter systems that deliver better results based on sound evaluation metrics, with a focus on case studies and best practices. Not the least, you will learn modern methods here to stay, designed by world-class expert and investor, Dr. Vincent Granville, founder of GenAItechLab.com.
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2024-07-31T18:41:41.000Z
2024-07-31T18:41:41.432Z
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/posts/vincentg64/304603661954264
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[ { "type": "text", "value": "โค๏ธโ€๐Ÿ”ฅย Just released version 2.0 of Argilla!", "raw": "โค๏ธโ€๐Ÿ”ฅย Just released version 2.0 of Argilla!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This small revolution includes:", "raw": "This small revolution includes:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ”Œย You can now integrate with the Hugging Face Hub and get started in under five minutes.", "raw": "๐Ÿ”Œย You can now integrate with the Hugging Face Hub and get started in under five minutes.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿช‚ย A single ", "raw": "๐Ÿช‚ย A single ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`Dataset`", "href": null, "resource": null, "url": null, "code": "Dataset", "user": null, "label": null, "lang": null }, { "type": "text", "value": " class is now designed to handle multiple tasks.", "raw": " class is now designed to handle multiple tasks.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ”งย Itโ€™s 100 times simpler to configure your dataset now with the new SDK!", "raw": "๐Ÿ”งย Itโ€™s 100 times simpler to configure your dataset now with the new SDK!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“–ย The documentation has been revamped to be cleaner and more user-friendly.", "raw": "๐Ÿ“–ย The documentation has been revamped to be cleaner and more user-friendly.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐ŸŒย  A new feature automates splitting annotation tasks among a team.", "raw": "๐ŸŒย  A new feature automates splitting annotation tasks among a team.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœ๏ธย The layout has been made more flexible to accommodate many use cases.", "raw": "โœ๏ธย The layout has been made more flexible to accommodate many use cases.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Check out the release highlights for more details: ", "raw": "Check out the release highlights for more details: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/argilla-io/argilla/releases/tag/v2.0.0", "href": "https://github.com/argilla-io/argilla/releases/tag/v2.0.0", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
โค๏ธโ€๐Ÿ”ฅย Just released version 2.0 of Argilla! This small revolution includes: ๐Ÿ”Œย You can now integrate with the Hugging Face Hub and get started in under five minutes. ๐Ÿช‚ย A single `Dataset` class is now designed to handle multiple tasks. ๐Ÿ”งย Itโ€™s 100 times simpler to configure your dataset now with the new SDK! ๐Ÿ“–ย The documentation has been revamped to be cleaner and more user-friendly. ๐ŸŒย  A new feature automates splitting annotation tasks among a team. โœ๏ธย The layout has been made more flexible to accommodate many use cases. Check out the release highlights for more details: https://github.com/argilla-io/argilla/releases/tag/v2.0.0
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2024-07-31T15:58:53.000Z
2024-08-03T07:52:43.049Z
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/posts/Ameeeee/742986348801279
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[ { "type": "text", "value": "๐—Ÿ๐—น๐—ฎ๐—บ๐—ฎ-๐Ÿฏ.๐Ÿญ ๐—บ๐—ผ๐—ฑ๐—ฒ๐—น๐˜€ ๐—ณ๐—ถ๐—ป๐—ฎ๐—น๐—น๐˜† ๐—ด๐—ฒ๐˜ ๐˜๐—ต๐—ฒ๐—ถ๐—ฟ ๐—–๐—ต๐—ฎ๐˜๐—ฏ๐—ผ๐˜ ๐—”๐—ฟ๐—ฒ๐—ป๐—ฎ ๐—ฟ๐—ฎ๐—ป๐—ธ๐—ถ๐—ป๐—ด ๐ŸŽ–๏ธ", "raw": "๐—Ÿ๐—น๐—ฎ๐—บ๐—ฎ-๐Ÿฏ.๐Ÿญ ๐—บ๐—ผ๐—ฑ๐—ฒ๐—น๐˜€ ๐—ณ๐—ถ๐—ป๐—ฎ๐—น๐—น๐˜† ๐—ด๐—ฒ๐˜ ๐˜๐—ต๐—ฒ๐—ถ๐—ฟ ๐—–๐—ต๐—ฎ๐˜๐—ฏ๐—ผ๐˜ ๐—”๐—ฟ๐—ฒ๐—ป๐—ฎ ๐—ฟ๐—ฎ๐—ป๐—ธ๐—ถ๐—ป๐—ด ๐ŸŽ–๏ธ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Given the impressive benchmarks published my Meta for their Llama-3.1 models, I was curious to see how these models would compare to top proprietary models on Chatbot Arena.", "raw": "Given the impressive benchmarks published my Meta for their Llama-3.1 models, I was curious to see how these models would compare to top proprietary models on Chatbot Arena.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Now we've got the results! LMSys released the ELO derived from thousands of user votes for the new models, and here are the rankings:", "raw": "Now we've got the results! LMSys released the ELO derived from thousands of user votes for the new models, and here are the rankings:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ’ฅ 405B Model ranks 5th overall, in front of GPT-4-turbo! But behind GPT-4o, Claude-3.5 Sonnet and Gemini-advanced.", "raw": "๐Ÿ’ฅ 405B Model ranks 5th overall, in front of GPT-4-turbo! But behind GPT-4o, Claude-3.5 Sonnet and Gemini-advanced.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ‘ 70B Model climbs up to 9th rank ! From 1206 โžก๏ธ 1244.", "raw": "๐Ÿ‘ 70B Model climbs up to 9th rank ! From 1206 โžก๏ธ 1244.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ‘ 8B Model improves from 1152 โžก๏ธ 1170.", "raw": "๐Ÿ‘ 8B Model improves from 1152 โžก๏ธ 1170.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœ… This confirms that Llama-3.1 is a good contender for any task: any of its 3 model size is much cheaper to run than equivalent proprietary models!", "raw": "โœ… This confirms that Llama-3.1 is a good contender for any task: any of its 3 model size is much cheaper to run than equivalent proprietary models!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "For instance, here are the inference prices for the top models;", "raw": "For instance, here are the inference prices for the top models;", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โžค GPT-4-Turbo inference price from OpenAI: $5/M input tokens, $15/M output tokens", "raw": "โžค GPT-4-Turbo inference price from OpenAI: $5/M input tokens, $15/M output tokens", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โžค Llama-3.1-405B from HF API (for testing only): 3$/M for input or output tokens (Source linked in the first comment)", "raw": "โžค Llama-3.1-405B from HF API (for testing only): 3$/M for input or output tokens (Source linked in the first comment)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โžค Llama-3.1-405B from HF API (for testing only): free โœจ", "raw": "โžค Llama-3.1-405B from HF API (for testing only): free โœจ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Get a head start on the HF API (resource by ", "raw": "Get a head start on the HF API (resource by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@andrewrreed", "href": null, "resource": null, "url": null, "code": null, "user": "andrewrreed", "label": null, "lang": null }, { "type": "text", "value": ") ๐Ÿ‘‰ ", "raw": ") ๐Ÿ‘‰ ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/learn/cookbook/enterprise_hub_serverless_inference_api", "href": "https://huggingface.co/learn/cookbook/enterprise_hub_serverless_inference_api", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
๐—Ÿ๐—น๐—ฎ๐—บ๐—ฎ-๐Ÿฏ.๐Ÿญ ๐—บ๐—ผ๐—ฑ๐—ฒ๐—น๐˜€ ๐—ณ๐—ถ๐—ป๐—ฎ๐—น๐—น๐˜† ๐—ด๐—ฒ๐˜ ๐˜๐—ต๐—ฒ๐—ถ๐—ฟ ๐—–๐—ต๐—ฎ๐˜๐—ฏ๐—ผ๐˜ ๐—”๐—ฟ๐—ฒ๐—ป๐—ฎ ๐—ฟ๐—ฎ๐—ป๐—ธ๐—ถ๐—ป๐—ด ๐ŸŽ–๏ธ Given the impressive benchmarks published my Meta for their Llama-3.1 models, I was curious to see how these models would compare to top proprietary models on Chatbot Arena. Now we've got the results! LMSys released the ELO derived from thousands of user votes for the new models, and here are the rankings: ๐Ÿ’ฅ 405B Model ranks 5th overall, in front of GPT-4-turbo! But behind GPT-4o, Claude-3.5 Sonnet and Gemini-advanced. ๐Ÿ‘ 70B Model climbs up to 9th rank ! From 1206 โžก๏ธ 1244. ๐Ÿ‘ 8B Model improves from 1152 โžก๏ธ 1170. โœ… This confirms that Llama-3.1 is a good contender for any task: any of its 3 model size is much cheaper to run than equivalent proprietary models! For instance, here are the inference prices for the top models; โžค GPT-4-Turbo inference price from OpenAI: $5/M input tokens, $15/M output tokens โžค Llama-3.1-405B from HF API (for testing only): 3$/M for input or output tokens (Source linked in the first comment) โžค Llama-3.1-405B from HF API (for testing only): free โœจ Get a head start on the HF API (resource by @andrewrreed) ๐Ÿ‘‰ https://huggingface.co/learn/cookbook/enterprise_hub_serverless_inference_api
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2024-07-31T15:30:37.000Z
2024-07-31T15:48:56.738Z
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[ { "type": "text", "value": "๐Ÿ’Ž I created some shiny new Argilla datasets to go along with the 2.0 release! ", "raw": "๐Ÿ’Ž I created some shiny new Argilla datasets to go along with the 2.0 release! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```python\nimport argilla as rg \n\nds = rg.Dataset.from_hub(\n \"argilla/multi-modal-vlm-visit-bench\"\n) \n```", "href": null, "resource": null, "url": null, "code": "import argilla as rg \n\nds = rg.Dataset.from_hub(\n \"argilla/multi-modal-vlm-visit-bench\"\n) ", "user": null, "label": null, "lang": "python" }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/argilla/argilla-v20-compatible-datasets-66a8e670f351acac61a0421c", "href": null, "resource": { "type": "collection", "id": "argilla/argilla-v20-compatible-datasets-66a8e670f351acac61a0421c", "discussionNum": null }, "url": "https://huggingface.co/collections/argilla/argilla-v20-compatible-datasets-66a8e670f351acac61a0421c", "code": null, "user": null, "label": null, "lang": null } ]
๐Ÿ’Ž I created some shiny new Argilla datasets to go along with the 2.0 release! ```python import argilla as rg ds = rg.Dataset.from_hub( "argilla/multi-modal-vlm-visit-bench" ) ``` https://huggingface.co/collections/argilla/argilla-v20-compatible-datasets-66a8e670f351acac61a0421c
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2024-07-31T14:45:45.000Z
2024-07-31T14:48:54.553Z
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/posts/davidberenstein1957/397656500149493
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[ { "type": "text", "value": "๐Ÿ”ฅ Today, Writer dropped Palmyra-Med-70b and Palmyra-Fin-70b, two new domain-specific models that are setting a new standard for medical and financial model performance.", "raw": "๐Ÿ”ฅ Today, Writer dropped Palmyra-Med-70b and Palmyra-Fin-70b, two new domain-specific models that are setting a new standard for medical and financial model performance.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "TL;DR", "raw": "TL;DR", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Palmyra-Med-70b", "raw": "Palmyra-Med-70b", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ”ข 8k and 32k versions available", "raw": "๐Ÿ”ข 8k and 32k versions available", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿš€ MMLU performance of ~86%, outperforming other top models", "raw": "๐Ÿš€ MMLU performance of ~86%, outperforming other top models", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ‘จโ€โš•๏ธ Great for diagnosing, planning treatments, medical research, insurance coding and billing", "raw": "๐Ÿ‘จโ€โš•๏ธ Great for diagnosing, planning treatments, medical research, insurance coding and billing", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“ƒ Open-model license for non-commercial use cases", "raw": "๐Ÿ“ƒ Open-model license for non-commercial use cases", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿค— Available on Hugging Face: ", "raw": "๐Ÿค— Available on Hugging Face: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/Writer/Palmyra-Med-70B", "href": null, "resource": { "type": "model", "id": "Writer/Palmyra-Med-70B", "discussionNum": null }, "url": "https://huggingface.co/Writer/Palmyra-Med-70B", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ’พ Live on NVIDIA NIM: ", "raw": "๐Ÿ’พ Live on NVIDIA NIM: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://build.nvidia.com/writer/palmyra-med-70b", "href": "https://build.nvidia.com/writer/palmyra-med-70b", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Palmyra-Fin-70b", "raw": "Palmyra-Fin-70b", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿš€ Passed the CFA Level III exam with a 73% score โ€” the first model to do so", "raw": "๐Ÿš€ Passed the CFA Level III exam with a 73% score โ€” the first model to do so", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ’ธ Skilled at complex tasks like investment research, financial analysis, and sentiment analysis", "raw": "๐Ÿ’ธ Skilled at complex tasks like investment research, financial analysis, and sentiment analysis", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“ˆ Outperformed other top models on a long-fin-eval test of real-world use cases", "raw": "๐Ÿ“ˆ Outperformed other top models on a long-fin-eval test of real-world use cases", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“ƒ Open-model license for non-commercial use cases", "raw": "๐Ÿ“ƒ Open-model license for non-commercial use cases", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿค— Available on Hugging Face: ", "raw": "๐Ÿค— Available on Hugging Face: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/Writer/Palmyra-Fin-70B-32K", "href": null, "resource": { "type": "model", "id": "Writer/Palmyra-Fin-70B-32K", "discussionNum": null }, "url": "https://huggingface.co/Writer/Palmyra-Fin-70B-32K", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ’พ Live on NVIDIA NIM: ", "raw": "๐Ÿ’พ Live on NVIDIA NIM: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://build.nvidia.com/writer/palmyra-fin-70b-32k", "href": "https://build.nvidia.com/writer/palmyra-fin-70b-32k", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Try them out and let us know what you think!", "raw": "Try them out and let us know what you think!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
๐Ÿ”ฅ Today, Writer dropped Palmyra-Med-70b and Palmyra-Fin-70b, two new domain-specific models that are setting a new standard for medical and financial model performance. TL;DR Palmyra-Med-70b ๐Ÿ”ข 8k and 32k versions available ๐Ÿš€ MMLU performance of ~86%, outperforming other top models ๐Ÿ‘จโ€โš•๏ธ Great for diagnosing, planning treatments, medical research, insurance coding and billing ๐Ÿ“ƒ Open-model license for non-commercial use cases ๐Ÿค— Available on Hugging Face: https://huggingface.co/Writer/Palmyra-Med-70B ๐Ÿ’พ Live on NVIDIA NIM: https://build.nvidia.com/writer/palmyra-med-70b Palmyra-Fin-70b ๐Ÿš€ Passed the CFA Level III exam with a 73% score โ€” the first model to do so ๐Ÿ’ธ Skilled at complex tasks like investment research, financial analysis, and sentiment analysis ๐Ÿ“ˆ Outperformed other top models on a long-fin-eval test of real-world use cases ๐Ÿ“ƒ Open-model license for non-commercial use cases ๐Ÿค— Available on Hugging Face: https://huggingface.co/Writer/Palmyra-Fin-70B-32K ๐Ÿ’พ Live on NVIDIA NIM: https://build.nvidia.com/writer/palmyra-fin-70b-32k Try them out and let us know what you think!
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2024-07-31T14:24:18.000Z
2024-08-02T15:06:01.906Z
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/posts/samjulien/584786234994856
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[ { "type": "text", "value": "Excited to announce the release of our high-quality Llama-3.1 8B 4-bit HQQ/calibrated quantized model! Achieving an impressive 99.3% relative performance to FP16, it also delivers the fastest inference speed for transformers. ", "raw": "Excited to announce the release of our high-quality Llama-3.1 8B 4-bit HQQ/calibrated quantized model! Achieving an impressive 99.3% relative performance to FP16, it also delivers the fastest inference speed for transformers. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/mobiuslabsgmbh/Llama-3.1-8b-instruct_4bitgs64_hqq_calib", "href": null, "resource": { "type": "model", "id": "mobiuslabsgmbh/Llama-3.1-8b-instruct_4bitgs64_hqq_calib", "discussionNum": null }, "url": "https://huggingface.co/mobiuslabsgmbh/Llama-3.1-8b-instruct_4bitgs64_hqq_calib", "code": null, "user": null, "label": null, "lang": null } ]
Excited to announce the release of our high-quality Llama-3.1 8B 4-bit HQQ/calibrated quantized model! Achieving an impressive 99.3% relative performance to FP16, it also delivers the fastest inference speed for transformers. https://huggingface.co/mobiuslabsgmbh/Llama-3.1-8b-instruct_4bitgs64_hqq_calib
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2024-07-31T13:18:19.000Z
2024-07-31T15:50:27.655Z
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/posts/appoose/199757608126776
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[ { "type": "text", "value": "Hey everyone ๐Ÿค—!", "raw": "Hey everyone ๐Ÿค—!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Check out this cool new space from Finegrain: ", "raw": "Check out this cool new space from Finegrain: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/finegrain/finegrain-object-eraser", "href": null, "resource": { "type": "space", "id": "finegrain/finegrain-object-eraser", "discussionNum": null }, "url": "https://huggingface.co/spaces/finegrain/finegrain-object-eraser", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Under the hoods, it's a pipeline of models (currently exposed via an API) that allows you to easily erase any object from your image just by naming it or selecting it! Not only will the object disappear, but so will its effects on the scene, like shadows and reflections. Built on top of Refiners, our micro-framework for simple foundation model adaptation (feel free to star it on GitHub if you like it: ", "raw": "Under the hoods, it's a pipeline of models (currently exposed via an API) that allows you to easily erase any object from your image just by naming it or selecting it! Not only will the object disappear, but so will its effects on the scene, like shadows and reflections. Built on top of Refiners, our micro-framework for simple foundation model adaptation (feel free to star it on GitHub if you like it: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/finegrain-ai/refiners", "href": "https://github.com/finegrain-ai/refiners", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ")", "raw": ")", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Hey everyone ๐Ÿค—! Check out this cool new space from Finegrain: https://huggingface.co/spaces/finegrain/finegrain-object-eraser Under the hoods, it's a pipeline of models (currently exposed via an API) that allows you to easily erase any object from your image just by naming it or selecting it! Not only will the object disappear, but so will its effects on the scene, like shadows and reflections. Built on top of Refiners, our micro-framework for simple foundation model adaptation (feel free to star it on GitHub if you like it: https://github.com/finegrain-ai/refiners)
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2024-07-31T13:04:56.000Z
2024-07-31T19:59:19.385Z
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/posts/1aurent/693098912127415
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460911864437039
[ { "type": "text", "value": "Journalists, this is a must-read for your career evolution. I just read ", "raw": "Journalists, this is a must-read for your career evolution. I just read ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@ndiakopoulos", "href": null, "resource": null, "url": null, "code": null, "user": "ndiakopoulos", "label": null, "lang": null }, { "type": "text", "value": " on \"The Impact of Generative AI on Journalistic Labor\". Here are my 5 takeaways:", "raw": " on \"The Impact of Generative AI on Journalistic Labor\". Here are my 5 takeaways:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿš€ LLMs could make 83% of reporter tasks and 76% of editor tasks way more efficient. \"Itโ€™s important to emphasize that these figures are fundamentally about augmentation rather than automation\".", "raw": "๐Ÿš€ LLMs could make 83% of reporter tasks and 76% of editor tasks way more efficient. \"Itโ€™s important to emphasize that these figures are fundamentally about augmentation rather than automation\".", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿงฉ Four emerging job clusters to consider: AI-doers, AI-users, AI-strategizers, and AI-reporters. Where do you fit?", "raw": "๐Ÿงฉ Four emerging job clusters to consider: AI-doers, AI-users, AI-strategizers, and AI-reporters. Where do you fit?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐ŸŒŠ US newsrooms \"will need people (1) who have the skills to use the current generation of LLMs and (2) who can develop the bespoke software to unlock their full potential, particularly if building new in-house tools\". Get ahead of the curve.", "raw": "๐ŸŒŠ US newsrooms \"will need people (1) who have the skills to use the current generation of LLMs and (2) who can develop the bespoke software to unlock their full potential, particularly if building new in-house tools\". Get ahead of the curve.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ”‘ Key takeaway: upskill in AI. It's not just about using tools, but understanding how to integrate them into your workflow.", "raw": "๐Ÿ”‘ Key takeaway: upskill in AI. It's not just about using tools, but understanding how to integrate them into your workflow.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“š News orgs \"would be wise to accelerate hiring and invest in upskilling their existing workforce\". Take advantage or seek out learning opportunities.", "raw": "๐Ÿ“š News orgs \"would be wise to accelerate hiring and invest in upskilling their existing workforce\". Take advantage or seek out learning opportunities.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Journalists: What AI skills are you planning to develop? How might this reshape your role?", "raw": "Journalists: What AI skills are you planning to develop? How might this reshape your role?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ‘‰ Read the full blog post here: ", "raw": "๐Ÿ‘‰ Read the full blog post here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://generative-ai-newsroom.com/the-impact-of-generative-ai-on-journalistic-labor-e87a6c333245", "href": "https://generative-ai-newsroom.com/the-impact-of-generative-ai-on-journalistic-labor-e87a6c333245", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " It's worth your time if you're thinking about the future of journalism & your career.", "raw": " It's worth your time if you're thinking about the future of journalism & your career.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "#AIinJournalism #CareerEvolution #FutureofNews", "raw": "#AIinJournalism #CareerEvolution #FutureofNews", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Journalists, this is a must-read for your career evolution. I just read @ndiakopoulos on "The Impact of Generative AI on Journalistic Labor". Here are my 5 takeaways: ๐Ÿš€ LLMs could make 83% of reporter tasks and 76% of editor tasks way more efficient. "Itโ€™s important to emphasize that these figures are fundamentally about augmentation rather than automation". ๐Ÿงฉ Four emerging job clusters to consider: AI-doers, AI-users, AI-strategizers, and AI-reporters. Where do you fit? ๐ŸŒŠ US newsrooms "will need people (1) who have the skills to use the current generation of LLMs and (2) who can develop the bespoke software to unlock their full potential, particularly if building new in-house tools". Get ahead of the curve. ๐Ÿ”‘ Key takeaway: upskill in AI. It's not just about using tools, but understanding how to integrate them into your workflow. ๐Ÿ“š News orgs "would be wise to accelerate hiring and invest in upskilling their existing workforce". Take advantage or seek out learning opportunities. Journalists: What AI skills are you planning to develop? How might this reshape your role? ๐Ÿ‘‰ Read the full blog post here: https://generative-ai-newsroom.com/the-impact-of-generative-ai-on-journalistic-labor-e87a6c333245 It's worth your time if you're thinking about the future of journalism & your career. #AIinJournalism #CareerEvolution #FutureofNews
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2024-07-31T12:45:57.000Z
2024-07-31T12:45:57.613Z
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/posts/fdaudens/460911864437039
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They should make a thing like google colab but you can have unlimited free access to a whole datacenter that would be cool. like if you agree
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2024-07-31T08:41:42.000Z
2024-08-05T20:48:13.236Z
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/posts/takeraparterer/328474988002537
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[ { "type": "text", "value": "๐—ง๐—ต๐—ฒ ๐—ต๐˜‚๐—ด๐—ฒ ๐—ฐ๐—ผ๐˜€๐˜ ๐—ผ๐—ณ ๐—ฟ๐—ฒ๐˜€๐—ฒ๐—ฎ๐—ฟ๐—ฐ๐—ต ๐—ผ๐—ป ๐—ณ๐—ฟ๐—ผ๐—ป๐˜๐—ถ๐—ฒ๐—ฟ ๐—Ÿ๐—Ÿ๐— ๐˜€ ๐Ÿ’ธ", "raw": "๐—ง๐—ต๐—ฒ ๐—ต๐˜‚๐—ด๐—ฒ ๐—ฐ๐—ผ๐˜€๐˜ ๐—ผ๐—ณ ๐—ฟ๐—ฒ๐˜€๐—ฒ๐—ฎ๐—ฟ๐—ฐ๐—ต ๐—ผ๐—ป ๐—ณ๐—ฟ๐—ผ๐—ป๐˜๐—ถ๐—ฒ๐—ฟ ๐—Ÿ๐—Ÿ๐— ๐˜€ ๐Ÿ’ธ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Google DeepMind recently released a great paper that shows optimal hyperparameters to train across different regimes: Scaling Exponents Across Parameterizations and Optimizers, with data from 10,000 training runs.", "raw": "Google DeepMind recently released a great paper that shows optimal hyperparameters to train across different regimes: Scaling Exponents Across Parameterizations and Optimizers, with data from 10,000 training runs.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "One engineer decided to quantify the price of such a large-scale experiment.", "raw": "One engineer decided to quantify the price of such a large-scale experiment.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ˜ฌ And the bill is hefty: ~13M USD ", "raw": "๐Ÿ˜ฌ And the bill is hefty: ~13M USD ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This exact number is to take with a grain of salt because many approximations were necessary to get the final result.", "raw": "This exact number is to take with a grain of salt because many approximations were necessary to get the final result.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โ›”๏ธ But still this ballpark means that for this sole experiment, the price is way over what most startups or research labs could afford.", "raw": "โ›”๏ธ But still this ballpark means that for this sole experiment, the price is way over what most startups or research labs could afford.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This means that open-sourcing research is more important than ever, to put everyone in the ecosystem on a roughly equal footing. Don't let OpenAI run first, they'll keep everything for themselves!", "raw": "This means that open-sourcing research is more important than ever, to put everyone in the ecosystem on a roughly equal footing. Don't let OpenAI run first, they'll keep everything for themselves!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Read the full post that quantifies the paper's cost ๐Ÿ‘‰ ", "raw": "Read the full post that quantifies the paper's cost ๐Ÿ‘‰ ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://152334h.github.io/blog/scaling-exponents/", "href": "https://152334h.github.io/blog/scaling-exponents/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
๐—ง๐—ต๐—ฒ ๐—ต๐˜‚๐—ด๐—ฒ ๐—ฐ๐—ผ๐˜€๐˜ ๐—ผ๐—ณ ๐—ฟ๐—ฒ๐˜€๐—ฒ๐—ฎ๐—ฟ๐—ฐ๐—ต ๐—ผ๐—ป ๐—ณ๐—ฟ๐—ผ๐—ป๐˜๐—ถ๐—ฒ๐—ฟ ๐—Ÿ๐—Ÿ๐— ๐˜€ ๐Ÿ’ธ Google DeepMind recently released a great paper that shows optimal hyperparameters to train across different regimes: Scaling Exponents Across Parameterizations and Optimizers, with data from 10,000 training runs. One engineer decided to quantify the price of such a large-scale experiment. ๐Ÿ˜ฌ And the bill is hefty: ~13M USD This exact number is to take with a grain of salt because many approximations were necessary to get the final result. โ›”๏ธ But still this ballpark means that for this sole experiment, the price is way over what most startups or research labs could afford. This means that open-sourcing research is more important than ever, to put everyone in the ecosystem on a roughly equal footing. Don't let OpenAI run first, they'll keep everything for themselves! Read the full post that quantifies the paper's cost ๐Ÿ‘‰ https://152334h.github.io/blog/scaling-exponents/
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2024-07-30T15:08:55.000Z
2024-07-31T06:31:41.589Z
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neuralmagic/Meta-Llama-3.1-405B-Instruct-FP8 Requant of the big llama, using 20% less memory https://huggingface.co/neuralmagic/Meta-Llama-3.1-405B-Instruct-FP8
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2024-07-30T15:03:52.000Z
2024-07-30T15:03:52.836Z
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/posts/KnutJaegersberg/675597604860526
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[ { "type": "text", "value": "With larger and larger diffusion transformers coming up, it's becoming increasingly important to have some good quantization tools for them.", "raw": "With larger and larger diffusion transformers coming up, it's becoming increasingly important to have some good quantization tools for them.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "We present our findings from a series of experiments on quantizing different diffusion pipelines based on diffusion transformers. ", "raw": "We present our findings from a series of experiments on quantizing different diffusion pipelines based on diffusion transformers. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "We demonstrate excellent memory savings with a bit of sacrifice on inference latency which is expected to improve in the coming days. ", "raw": "We demonstrate excellent memory savings with a bit of sacrifice on inference latency which is expected to improve in the coming days. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Diffusers ๐Ÿค Quanto โค๏ธ", "raw": "Diffusers ๐Ÿค Quanto โค๏ธ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This was a juicy collaboration between ", "raw": "This was a juicy collaboration between ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@dacorvo", "href": null, "resource": null, "url": null, "code": null, "user": "dacorvo", "label": null, "lang": null }, { "type": "text", "value": " and myself. ", "raw": " and myself. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Check out the post to learn all about it", "raw": "Check out the post to learn all about it", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/quanto-diffusers", "href": "https://huggingface.co/blog/quanto-diffusers", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
With larger and larger diffusion transformers coming up, it's becoming increasingly important to have some good quantization tools for them. We present our findings from a series of experiments on quantizing different diffusion pipelines based on diffusion transformers. We demonstrate excellent memory savings with a bit of sacrifice on inference latency which is expected to improve in the coming days. Diffusers ๐Ÿค Quanto โค๏ธ This was a juicy collaboration between @dacorvo and myself. Check out the post to learn all about it https://huggingface.co/blog/quanto-diffusers
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2024-07-31T18:11:01.286Z
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[ { "type": "text", "value": "Kling AI Video is FINALLY Public (All Countries), Free to Use and MIND BLOWING - Full Tutorial > ", "raw": "Kling AI Video is FINALLY Public (All Countries), Free to Use and MIND BLOWING - Full Tutorial > ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://youtu.be/zcpqAxYV1_w", "href": "https://youtu.be/zcpqAxYV1_w", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "You probably seen those mind blowing AI made videos. And the day has arrived. The famous Kling AI is now worldwide available for free. In this tutorial video I will show you how to register for free with just email to Kling AI and use its mind blowing text to video animation, image to video animation and text to image, and image to image capabilities. This video will show you non-cherry pick results so you will know the actual quality and capability of the model unlike those extremely cherry pick example demos. Still, #KlingAI is the only #AI model that competes with OpenAI's #SORA and it is real to use.", "raw": "You probably seen those mind blowing AI made videos. And the day has arrived. The famous Kling AI is now worldwide available for free. In this tutorial video I will show you how to register for free with just email to Kling AI and use its mind blowing text to video animation, image to video animation and text to image, and image to image capabilities. This video will show you non-cherry pick results so you will know the actual quality and capability of the model unlike those extremely cherry pick example demos. Still, #KlingAI is the only #AI model that competes with OpenAI's #SORA and it is real to use.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ”— Kling AI Official Website โคต๏ธ", "raw": "๐Ÿ”— Kling AI Official Website โคต๏ธ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โ–ถ๏ธ ", "raw": "โ–ถ๏ธ ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.klingai.com/", "href": "https://www.klingai.com/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ”— SECourses Discord Channel to Get Full Support โคต๏ธ", "raw": "๐Ÿ”— SECourses Discord Channel to Get Full Support โคต๏ธ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โ–ถ๏ธ ", "raw": "โ–ถ๏ธ ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://discord.com/servers/software-engineering-courses-secourses-772774097734074388", "href": "https://discord.com/servers/software-engineering-courses-secourses-772774097734074388", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ”— Our GitHub Repository โคต๏ธ", "raw": "๐Ÿ”— Our GitHub Repository โคต๏ธ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โ–ถ๏ธ ", "raw": "โ–ถ๏ธ ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/FurkanGozukara/Stable-Diffusion", "href": "https://github.com/FurkanGozukara/Stable-Diffusion", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ”— Our Reddit โคต๏ธ", "raw": "๐Ÿ”— Our Reddit โคต๏ธ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โ–ถ๏ธ ", "raw": "โ–ถ๏ธ ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.reddit.com/r/SECourses/", "href": "https://www.reddit.com/r/SECourses/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Kling AI Video is FINALLY Public (All Countries), Free to Use and MIND BLOWING - Full Tutorial > https://youtu.be/zcpqAxYV1_w You probably seen those mind blowing AI made videos. And the day has arrived. The famous Kling AI is now worldwide available for free. In this tutorial video I will show you how to register for free with just email to Kling AI and use its mind blowing text to video animation, image to video animation and text to image, and image to image capabilities. This video will show you non-cherry pick results so you will know the actual quality and capability of the model unlike those extremely cherry pick example demos. Still, #KlingAI is the only #AI model that competes with OpenAI's #SORA and it is real to use. ๐Ÿ”— Kling AI Official Website โคต๏ธ โ–ถ๏ธ https://www.klingai.com/ ๐Ÿ”— SECourses Discord Channel to Get Full Support โคต๏ธ โ–ถ๏ธ https://discord.com/servers/software-engineering-courses-secourses-772774097734074388 ๐Ÿ”— Our GitHub Repository โคต๏ธ โ–ถ๏ธ https://github.com/FurkanGozukara/Stable-Diffusion ๐Ÿ”— Our Reddit โคต๏ธ โ–ถ๏ธ https://www.reddit.com/r/SECourses/
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2024-07-30T00:53:00.000Z
2024-10-29T09:09:01.719Z
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[ { "type": "text", "value": "Looking for a combination of speed and quality? Look no further! I've created a space that merges Open WebUI's excellent interface and features with the lightning-fast performance of the Groq API. Experience top-tier models in no time. Try it out for free here:", "raw": "Looking for a combination of speed and quality? Look no further! I've created a space that merges Open WebUI's excellent interface and features with the lightning-fast performance of the Groq API. Experience top-tier models in no time. Try it out for free here:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/L-AI/groq-chat", "href": null, "resource": { "type": "space", "id": "L-AI/groq-chat", "discussionNum": null }, "url": "https://huggingface.co/spaces/L-AI/groq-chat", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "\"A big thank you to Groq for providing their fantastic API at no cost!\"", "raw": "\"A big thank you to Groq for providing their fantastic API at no cost!\"", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Looking for a combination of speed and quality? Look no further! I've created a space that merges Open WebUI's excellent interface and features with the lightning-fast performance of the Groq API. Experience top-tier models in no time. Try it out for free here: https://huggingface.co/spaces/L-AI/groq-chat "A big thank you to Groq for providing their fantastic API at no cost!"
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2024-07-29T16:33:35.000Z
2024-07-29T16:33:35.989Z
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/posts/Artples/551816601997074
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@MaartenGr nice post https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-quantization ("A Visual Guide to Quantization") Would it make sense for you to publish it here too?
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2024-07-29T15:09:26.000Z
2024-07-30T07:38:52.530Z
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[ { "type": "text", "value": "I am looking for anotation tool/software for video segmentations, landmarking and feature-detection. ", "raw": "I am looking for anotation tool/software for video segmentations, landmarking and feature-detection. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I had bumped into xlabelAnything, however I could not run it on my machine.", "raw": "I had bumped into xlabelAnything, however I could not run it on my machine.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " Any recommendations ?", "raw": " Any recommendations ?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
I am looking for anotation tool/software for video segmentations, landmarking and feature-detection. I had bumped into xlabelAnything, however I could not run it on my machine. Any recommendations ?
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2024-07-29T13:05:00.000Z
2024-07-30T04:19:37.516Z
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/posts/Saugatkafley/403721444206150
893
1
819761092372121
[ { "type": "text", "value": "Hey ! I'm working on a 100% synthetic Dataset Hub here (you can search for any kind of datasets an the app invents them). The link is here: ", "raw": "Hey ! I'm working on a 100% synthetic Dataset Hub here (you can search for any kind of datasets an the app invents them). The link is here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub", "href": null, "resource": { "type": "space", "id": "infinite-dataset-hub/infinite-dataset-hub", "discussionNum": null }, "url": "https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Question for the Community:", "raw": "Question for the Community:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Which models should I use to generate images and audio samples for those datasets ? ๐Ÿค—", "raw": "Which models should I use to generate images and audio samples for those datasets ? ๐Ÿค—", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Hey ! I'm working on a 100% synthetic Dataset Hub here (you can search for any kind of datasets an the app invents them). The link is here: https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub Question for the Community: Which models should I use to generate images and audio samples for those datasets ? ๐Ÿค—
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[]
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2024-07-29T09:46:33.000Z
2024-07-31T11:21:01.159Z
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/posts/lhoestq/819761092372121
3,924
4
446225760997052
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Just made this! https://github.com/takeraparterer/Charformer
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2024-07-29T06:57:58.000Z
2024-08-05T22:15:53.957Z
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/posts/takeraparterer/446225760997052
3,215
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762807238717942
[ { "type": "resource", "value": null, "raw": "https://huggingface.co/nevmenandr/w2v-chess", "href": null, "resource": { "type": "model", "id": "nevmenandr/w2v-chess", "discussionNum": null }, "url": "https://huggingface.co/nevmenandr/w2v-chess", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```python\nimport gensim\nfrom sklearn.decomposition import PCA\nimport matplotlib\nimport matplotlib.pyplot as plt\n\nmodel = gensim.models.Word2Vec.load('white_moves.model')\ndict_moves = model.wv.vocab\ndict_moves_appr = {}\nfor k in dict_moves:\n if not k.startswith('->'):\n continue\n dict_moves_appr[k] = dict_moves[k]\nX = model[model.wv.vocab]\npca = PCA(n_components=2)\nresult = pca.fit_transform(X)\nfig, ax = plt.subplots()\nax.plot(Y[:, 0], Y[:, 1], 'o')\nax.set_title('White moves')\nlab = list(dict_moves_appr)\nfor i, lb in enumerate(lab):\n plt.annotate(lb, xy=(Y[i, 0], Y[i, 1]))\nplt.show()\n```", "href": null, "resource": null, "url": null, "code": "import gensim\nfrom sklearn.decomposition import PCA\nimport matplotlib\nimport matplotlib.pyplot as plt\n\nmodel = gensim.models.Word2Vec.load('white_moves.model')\ndict_moves = model.wv.vocab\ndict_moves_appr = {}\nfor k in dict_moves:\n if not k.startswith('->'):\n continue\n dict_moves_appr[k] = dict_moves[k]\nX = model[model.wv.vocab]\npca = PCA(n_components=2)\nresult = pca.fit_transform(X)\nfig, ax = plt.subplots()\nax.plot(Y[:, 0], Y[:, 1], 'o')\nax.set_title('White moves')\nlab = list(dict_moves_appr)\nfor i, lb in enumerate(lab):\n plt.annotate(lb, xy=(Y[i, 0], Y[i, 1]))\nplt.show()", "user": null, "label": null, "lang": "python" }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "biblically accurate angel", "raw": "biblically accurate angel", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
https://huggingface.co/nevmenandr/w2v-chess ```python import gensim from sklearn.decomposition import PCA import matplotlib import matplotlib.pyplot as plt model = gensim.models.Word2Vec.load('white_moves.model') dict_moves = model.wv.vocab dict_moves_appr = {} for k in dict_moves: if not k.startswith('->'): continue dict_moves_appr[k] = dict_moves[k] X = model[model.wv.vocab] pca = PCA(n_components=2) result = pca.fit_transform(X) fig, ax = plt.subplots() ax.plot(Y[:, 0], Y[:, 1], 'o') ax.set_title('White moves') lab = list(dict_moves_appr) for i, lb in enumerate(lab): plt.annotate(lb, xy=(Y[i, 0], Y[i, 1])) plt.show() ``` biblically accurate angel
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[]
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2024-07-28T22:41:34.000Z
2024-07-28T22:41:34.324Z
[]
/posts/nevmenandr/762807238717942
2,613
0
987006720529829
[ { "type": "text", "value": "Just published Image Captioning Editor Gradio APP - Edit Your Captions Super Easy Including Batch Editing - For Windows, RunPod and Massed Compute. Developed by me. Have lots of amazing features that all you need. Let me know if missing any features. ", "raw": "Just published Image Captioning Editor Gradio APP - Edit Your Captions Super Easy Including Batch Editing - For Windows, RunPod and Massed Compute. Developed by me. Have lots of amazing features that all you need. Let me know if missing any features. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Gradio is amazing to develop such amazing apps in short time. Used Claude 3.5 to develop it :)", "raw": "Gradio is amazing to develop such amazing apps in short time. Used Claude 3.5 to develop it :)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Scripts are available here : ", "raw": "Scripts are available here : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.patreon.com/posts/108992085", "href": "https://www.patreon.com/posts/108992085", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Just published Image Captioning Editor Gradio APP - Edit Your Captions Super Easy Including Batch Editing - For Windows, RunPod and Massed Compute. Developed by me. Have lots of amazing features that all you need. Let me know if missing any features. Gradio is amazing to develop such amazing apps in short time. Used Claude 3.5 to develop it :) Scripts are available here : https://www.patreon.com/posts/108992085
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[]
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2024-07-28T22:00:30.000Z
2024-07-28T22:00:30.266Z
[]
/posts/MonsterMMORPG/987006720529829
1,817
0
161498281853215
[ { "type": "text", "value": "lamagenius", "raw": "lamagenius", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Smart free open source AI powered search engine", "raw": "Smart free open source AI powered search engine", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://hf.co/chat/assistant/66a5fc9f02b1826ba4cabd72", "href": "https://hf.co/chat/assistant/66a5fc9f02b1826ba4cabd72", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
lamagenius Smart free open source AI powered search engine https://hf.co/chat/assistant/66a5fc9f02b1826ba4cabd72
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[]
[]
2024-07-28T08:24:06.000Z
2024-07-28T18:18:42.236Z
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/posts/Taf2023/161498281853215
904
2
681081193566580
[ { "type": "text", "value": "Hi HF Community!๐Ÿค—", "raw": "Hi HF Community!๐Ÿค—", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "In the past days, OpenAI announced their search engine, SearchGPT: today, I'm glad to introduce you SearchPhi, an AI-powered and open-source web search tool that aims to reproduce similar features to SearchGPT, built upon ", "raw": "In the past days, OpenAI announced their search engine, SearchGPT: today, I'm glad to introduce you SearchPhi, an AI-powered and open-source web search tool that aims to reproduce similar features to SearchGPT, built upon ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct", "href": null, "resource": { "type": "model", "id": "microsoft/Phi-3-mini-4k-instruct", "discussionNum": null }, "url": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ", llama.cpp๐Ÿฆ™ and Streamlit. ", "raw": ", llama.cpp๐Ÿฆ™ and Streamlit. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Although not as capable as SearchGPT, SearchPhi v0.0-beta.0 is a first step toward a fully functional and multimodal search engine :)", "raw": "Although not as capable as SearchGPT, SearchPhi v0.0-beta.0 is a first step toward a fully functional and multimodal search engine :)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "If you want to know more, head over to the GitHub repository (", "raw": "If you want to know more, head over to the GitHub repository (", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/AstraBert/SearchPhi", "href": "https://github.com/AstraBert/SearchPhi", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ") and, to test it out, use this HF space: ", "raw": ") and, to test it out, use this HF space: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/as-cle-bert/SearchPhi", "href": null, "resource": { "type": "space", "id": "as-cle-bert/SearchPhi", "discussionNum": null }, "url": "https://huggingface.co/spaces/as-cle-bert/SearchPhi", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Have fun!๐Ÿฑ", "raw": "Have fun!๐Ÿฑ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Hi HF Community!๐Ÿค— In the past days, OpenAI announced their search engine, SearchGPT: today, I'm glad to introduce you SearchPhi, an AI-powered and open-source web search tool that aims to reproduce similar features to SearchGPT, built upon https://huggingface.co/microsoft/Phi-3-mini-4k-instruct, llama.cpp๐Ÿฆ™ and Streamlit. Although not as capable as SearchGPT, SearchPhi v0.0-beta.0 is a first step toward a fully functional and multimodal search engine :) If you want to know more, head over to the GitHub repository (https://github.com/AstraBert/SearchPhi) and, to test it out, use this HF space: https://huggingface.co/spaces/as-cle-bert/SearchPhi Have fun!๐Ÿฑ
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2024-07-28T06:54:26.000Z
2024-07-28T06:54:26.641Z
[]
/posts/as-cle-bert/681081193566580
5,048
0
203981221653529
[ { "type": "text", "value": "Datasets are down, I offer a solution", "raw": "Datasets are down, I offer a solution", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```\ngit lfs install\n```", "href": null, "resource": null, "url": null, "code": "git lfs install", "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```\ngit clone https://huggingface.co/datasets/{dataset/id}\n```", "href": null, "resource": null, "url": null, "code": "git clone https://huggingface.co/datasets/{dataset/id}", "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```python\nfrom datasets import load_dataset\n\ndataset = load_dataset(\"id\")\n```", "href": null, "resource": null, "url": null, "code": "from datasets import load_dataset\n\ndataset = load_dataset(\"id\")", "user": null, "label": null, "lang": "python" } ]
Datasets are down, I offer a solution ``` git lfs install ``` ``` git clone https://huggingface.co/datasets/{dataset/id} ``` ```python from datasets import load_dataset dataset = load_dataset("id") ```
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2024-07-27T17:46:14.000Z
2024-07-27T17:46:14.924Z
[]
/posts/nroggendorff/203981221653529
4,080
0
868685803350724
[ { "type": "text", "value": "โš—๏ธ Find reusable synthetic data pipeline code and corresponding datasets on the ", "raw": "โš—๏ธ Find reusable synthetic data pipeline code and corresponding datasets on the ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@huggingface", "href": null, "resource": null, "url": null, "code": null, "user": "huggingface", "label": null, "lang": null }, { "type": "text", "value": " Hub.", "raw": " Hub.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Find your pipline and use ", "raw": "Find your pipline and use ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`$ distilabel pipeline run --config \"hugging_face_dataset_url/pipeline.yaml\"`", "href": null, "resource": null, "url": null, "code": "$ distilabel pipeline run --config \"hugging_face_dataset_url/pipeline.yaml\"", "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Some components I used", "raw": "Some components I used", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Embedded dataset viewer ", "raw": "- Embedded dataset viewer ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/docs/hub/main/en/datasets-viewer-embed", "href": "https://huggingface.co/docs/hub/main/en/datasets-viewer-embed", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Hugging Face fsspec ", "raw": "- Hugging Face fsspec ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/docs/huggingface_hub/main/en/guides/hf_file_system", "href": "https://huggingface.co/docs/huggingface_hub/main/en/guides/hf_file_system", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- distilabel ", "raw": "- distilabel ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://distilabel.argilla.io/latest/", "href": "https://distilabel.argilla.io/latest/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Gradio leaderboard by Freddy Boulton ", "raw": "- Gradio leaderboard by Freddy Boulton ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/freddyaboulton/gradio_leaderboard", "href": null, "resource": { "type": "space", "id": "freddyaboulton/gradio_leaderboard", "discussionNum": null }, "url": "https://huggingface.co/spaces/freddyaboulton/gradio_leaderboard", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Gradio modal by Ali Abid", "raw": "- Gradio modal by Ali Abid", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Space: ", "raw": "Space: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/davidberenstein1957/distilabel-synthetic-data-pipeline-explorer", "href": null, "resource": { "type": "space", "id": "davidberenstein1957/distilabel-synthetic-data-pipeline-explorer", "discussionNum": null }, "url": "https://huggingface.co/spaces/davidberenstein1957/distilabel-synthetic-data-pipeline-explorer", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
โš—๏ธ Find reusable synthetic data pipeline code and corresponding datasets on the @huggingface Hub. Find your pipline and use `$ distilabel pipeline run --config "hugging_face_dataset_url/pipeline.yaml"` Some components I used - Embedded dataset viewer https://huggingface.co/docs/hub/main/en/datasets-viewer-embed - Hugging Face fsspec https://huggingface.co/docs/huggingface_hub/main/en/guides/hf_file_system - distilabel https://distilabel.argilla.io/latest/ - Gradio leaderboard by Freddy Boulton https://huggingface.co/spaces/freddyaboulton/gradio_leaderboard - Gradio modal by Ali Abid Space: https://huggingface.co/spaces/davidberenstein1957/distilabel-synthetic-data-pipeline-explorer
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2024-07-27T08:31:55.000Z
2024-07-29T09:40:40.794Z
[]
/posts/davidberenstein1957/868685803350724
2,401
1
726196603155745
[ { "type": "text", "value": "Hello, HuggingFace๐Ÿค— community ๐ŸŒŸ,", "raw": "Hello, HuggingFace๐Ÿค— community ๐ŸŒŸ,", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "All the amazing people quantising LLMs to AWQ and GPTQ ๐Ÿ”ง๐Ÿค–", "raw": "All the amazing people quantising LLMs to AWQ and GPTQ ๐Ÿ”ง๐Ÿค–", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Can you please mention the perplexity you achieved ๐Ÿ“‰ OR any other metric to measure the quantisation qualitatively? ๐Ÿ“Š", "raw": "Can you please mention the perplexity you achieved ๐Ÿ“‰ OR any other metric to measure the quantisation qualitatively? ๐Ÿ“Š", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The GGUF community follows this really well! ๐Ÿ‘", "raw": "The GGUF community follows this really well! ๐Ÿ‘", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "And if it is not too much to ask, the script used for quantisation would be amazing! ๐Ÿ“", "raw": "And if it is not too much to ask, the script used for quantisation would be amazing! ๐Ÿ“", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Thanks for the quants for the GPU poor! ๐Ÿ’ป", "raw": "Thanks for the quants for the GPU poor! ๐Ÿ’ป", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Hello, HuggingFace๐Ÿค— community ๐ŸŒŸ, All the amazing people quantising LLMs to AWQ and GPTQ ๐Ÿ”ง๐Ÿค– Can you please mention the perplexity you achieved ๐Ÿ“‰ OR any other metric to measure the quantisation qualitatively? ๐Ÿ“Š The GGUF community follows this really well! ๐Ÿ‘ And if it is not too much to ask, the script used for quantisation would be amazing! ๐Ÿ“ Thanks for the quants for the GPU poor! ๐Ÿ’ป
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2024-07-27T06:44:41.000Z
2024-07-29T00:34:02.001Z
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/posts/singhsidhukuldeep/726196603155745
2,346
1
851286980273412
[ { "type": "text", "value": "Exciting news!", "raw": "Exciting news!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "After a long wait, Ikari and me finally made a new release of our last model on NeverSleep repo: Lumimaid-v0.2", "raw": "After a long wait, Ikari and me finally made a new release of our last model on NeverSleep repo: Lumimaid-v0.2", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This model can be used in different size, from the small Llama-3.1-8B to the gigantic Mistral-Large-123B, finetuned by us.", "raw": "This model can be used in different size, from the small Llama-3.1-8B to the gigantic Mistral-Large-123B, finetuned by us.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Try them now!", "raw": "Try them now!", "href": null, "resource": null, "url": null, "code": null, "user": null, 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"raw": "- ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/NeverSleep/Lumimaid-v0.2-12B", "href": null, "resource": { "type": "model", "id": "NeverSleep/Lumimaid-v0.2-12B", "discussionNum": null }, "url": "https://huggingface.co/NeverSleep/Lumimaid-v0.2-12B", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- ", "raw": "- ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/NeverSleep/Lumimaid-v0.2-70B", "href": null, "resource": { "type": "model", "id": "NeverSleep/Lumimaid-v0.2-70B", "discussionNum": null }, "url": "https://huggingface.co/NeverSleep/Lumimaid-v0.2-70B", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- ", "raw": "- ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/NeverSleep/Lumimaid-v0.2-123B", "href": null, "resource": { "type": "model", "id": "NeverSleep/Lumimaid-v0.2-123B", "discussionNum": null }, "url": "https://huggingface.co/NeverSleep/Lumimaid-v0.2-123B", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "All the datasets we used will be added and credit will be given!", "raw": "All the datasets we used will be added and credit will be given!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "For the quant, we wait for fix to be applied (", "raw": "For the quant, we wait for fix to be applied (", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/ggerganov/llama.cpp/pull/8676", "href": "https://github.com/ggerganov/llama.cpp/pull/8676", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ")", "raw": ")", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Hope you will enjoy them!", "raw": "Hope you will enjoy them!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Exciting news! After a long wait, Ikari and me finally made a new release of our last model on NeverSleep repo: Lumimaid-v0.2 This model can be used in different size, from the small Llama-3.1-8B to the gigantic Mistral-Large-123B, finetuned by us. Try them now! - https://huggingface.co/NeverSleep/Lumimaid-v0.2-8B - https://huggingface.co/NeverSleep/Lumimaid-v0.2-12B - https://huggingface.co/NeverSleep/Lumimaid-v0.2-70B - https://huggingface.co/NeverSleep/Lumimaid-v0.2-123B All the datasets we used will be added and credit will be given! For the quant, we wait for fix to be applied (https://github.com/ggerganov/llama.cpp/pull/8676) Hope you will enjoy them!
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2024-07-26T18:10:57.000Z
2024-09-10T09:40:24.374Z
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/posts/Undi95/851286980273412
11,939
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328466287104951
[ { "type": "text", "value": "I can't resist an opportunity to update an old baseline. Read a new article on my latest look at improving MobileNet-V1 and EfficientNet-B0 baselines.", "raw": "I can't resist an opportunity to update an old baseline. Read a new article on my latest look at improving MobileNet-V1 and EfficientNet-B0 baselines.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/rwightman/mobilenet-baselines", "href": "https://huggingface.co/blog/rwightman/mobilenet-baselines", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/timm/mobilenetv1_100.ra4_e3600_r224_in1k", "href": null, "resource": { "type": "model", "id": "timm/mobilenetv1_100.ra4_e3600_r224_in1k", "discussionNum": null }, "url": "https://huggingface.co/timm/mobilenetv1_100.ra4_e3600_r224_in1k", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/timm/efficientnet_b0.ra4_e3600_r224_in1k", "href": null, "resource": { "type": "model", "id": "timm/efficientnet_b0.ra4_e3600_r224_in1k", "discussionNum": null }, "url": "https://huggingface.co/timm/efficientnet_b0.ra4_e3600_r224_in1k", "code": null, "user": null, "label": null, "lang": null } ]
I can't resist an opportunity to update an old baseline. Read a new article on my latest look at improving MobileNet-V1 and EfficientNet-B0 baselines. https://huggingface.co/blog/rwightman/mobilenet-baselines https://huggingface.co/timm/mobilenetv1_100.ra4_e3600_r224_in1k https://huggingface.co/timm/efficientnet_b0.ra4_e3600_r224_in1k
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[]
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2024-07-26T16:33:36.000Z
2024-07-26T16:50:49.372Z
[]
/posts/rwightman/328466287104951
1,987
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119699878001376
[ { "type": "text", "value": "At Hugging Face we have an open-source Cookbook with many applied AI recipes ๐Ÿ“–", "raw": "At Hugging Face we have an open-source Cookbook with many applied AI recipes ๐Ÿ“–", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Here are some of the latest recipes contributed โฅฅ", "raw": "Here are some of the latest recipes contributed โฅฅ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- \"Information Extraction with Haystack and NuExtract\": Use Haystack and transformers to build structured data extraction pipelines using LLMs by ", "raw": "- \"Information Extraction with Haystack and NuExtract\": Use Haystack and transformers to build structured data extraction pipelines using LLMs by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@anakin87", "href": null, "resource": null, "url": null, "code": null, "user": "anakin87", "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/learn/cookbook/en/information_extraction_haystack_nuextract", "href": "https://huggingface.co/learn/cookbook/en/information_extraction_haystack_nuextract", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- \"Build RAG with Hugging Face and Milvus\": Learn how to use Milvus with sentence transformers to build RAG pipelines ", "raw": "- \"Build RAG with Hugging Face and Milvus\": Learn how to use Milvus with sentence transformers to build RAG pipelines ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/learn/cookbook/rag_with_hf_and_milvus", "href": "https://huggingface.co/learn/cookbook/rag_with_hf_and_milvus", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- \"Code Search with Vector Embeddings and Qdrant\": Search a codebase by building a retrieval pipeline using Qdrant and sentence transformers ", "raw": "- \"Code Search with Vector Embeddings and Qdrant\": Search a codebase by building a retrieval pipeline using Qdrant and sentence transformers ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/learn/cookbook/code_search", "href": "https://huggingface.co/learn/cookbook/code_search", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Data analyst agent: get your dataโ€™s insights in the blink of an eye โœจ: great recipe by our own ", "raw": "- Data analyst agent: get your dataโ€™s insights in the blink of an eye โœจ: great recipe by our own ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@m-ric", "href": null, "resource": null, "url": null, "code": null, "user": "m-ric", "label": null, "lang": null }, { "type": "text", "value": " showing how to build an agent that can do data analysis! ๐Ÿ˜ฑ ", "raw": " showing how to build an agent that can do data analysis! ๐Ÿ˜ฑ ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/learn/cookbook/agent_data_analyst", "href": "https://huggingface.co/learn/cookbook/agent_data_analyst", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
At Hugging Face we have an open-source Cookbook with many applied AI recipes ๐Ÿ“– Here are some of the latest recipes contributed โฅฅ - "Information Extraction with Haystack and NuExtract": Use Haystack and transformers to build structured data extraction pipelines using LLMs by @anakin87 https://huggingface.co/learn/cookbook/en/information_extraction_haystack_nuextract - "Build RAG with Hugging Face and Milvus": Learn how to use Milvus with sentence transformers to build RAG pipelines https://huggingface.co/learn/cookbook/rag_with_hf_and_milvus - "Code Search with Vector Embeddings and Qdrant": Search a codebase by building a retrieval pipeline using Qdrant and sentence transformers https://huggingface.co/learn/cookbook/code_search - Data analyst agent: get your dataโ€™s insights in the blink of an eye โœจ: great recipe by our own @m-ric showing how to build an agent that can do data analysis! ๐Ÿ˜ฑ https://huggingface.co/learn/cookbook/agent_data_analyst
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2024-07-26T15:42:03.000Z
2024-07-26T15:42:03.653Z
[]
/posts/merve/119699878001376
3,656
0
454864775422313
[ { "type": "text", "value": "Barefoot developer experiment: AI-powered app creation with my poor coding skills ๐Ÿง ๐Ÿ’ป", "raw": "Barefoot developer experiment: AI-powered app creation with my poor coding skills ๐Ÿง ๐Ÿ’ป", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I recently discussed the \"barefoot developer\" concept - using AI to build apps for specific needs without coding expertise. Decided to put it to the test. ๐Ÿ”ฌ", "raw": "I recently discussed the \"barefoot developer\" concept - using AI to build apps for specific needs without coding expertise. Decided to put it to the test. ๐Ÿ”ฌ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ…โฒ๏ธ The challenge: Create a menu bar Pomodoro app for my computer to boost my focus. Previous attempt? Messy. ", "raw": "๐Ÿ…โฒ๏ธ The challenge: Create a menu bar Pomodoro app for my computer to boost my focus. Previous attempt? Messy. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿคฏ The twist: I've never coded in Swift. ", "raw": "๐Ÿคฏ The twist: I've never coded in Swift. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โšก๏ธ The result: 30 minutes. No joke. Elegant, functional, and shareable. ", "raw": "โšก๏ธ The result: 30 minutes. No joke. Elegant, functional, and shareable. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ”— Want to try it yourself? Grab the open source code and app here: ", "raw": "๐Ÿ”— Want to try it yourself? Grab the open source code and app here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Code: ", "raw": "- Code: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/fdaudens/pomodoro2", "href": "https://github.com/fdaudens/pomodoro2", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- App: ", "raw": "- App: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/fdaudens/pomodoro2/releases/tag/v1.0.0", "href": "https://github.com/fdaudens/pomodoro2/releases/tag/v1.0.0", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Key takeaways:", "raw": "Key takeaways:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿš€ AI-assisted development is evolving rapidly ", "raw": "๐Ÿš€ AI-assisted development is evolving rapidly ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿงฉ Domain expertise + AI tools can yield impressive results ", "raw": "๐Ÿงฉ Domain expertise + AI tools can yield impressive results ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐ŸŒ This approach democratizes app creation ", "raw": "๐ŸŒ This approach democratizes app creation ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿค” What's your take on AI-powered development? Have you experimented with it? ", "raw": "๐Ÿค” What's your take on AI-powered development? Have you experimented with it? ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "#AIinDevelopment #BarefootDeveloper #OpenSource", "raw": "#AIinDevelopment #BarefootDeveloper #OpenSource", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Barefoot developer experiment: AI-powered app creation with my poor coding skills ๐Ÿง ๐Ÿ’ป I recently discussed the "barefoot developer" concept - using AI to build apps for specific needs without coding expertise. Decided to put it to the test. ๐Ÿ”ฌ ๐Ÿ…โฒ๏ธ The challenge: Create a menu bar Pomodoro app for my computer to boost my focus. Previous attempt? Messy. ๐Ÿคฏ The twist: I've never coded in Swift. โšก๏ธ The result: 30 minutes. No joke. Elegant, functional, and shareable. ๐Ÿ”— Want to try it yourself? Grab the open source code and app here: - Code: https://github.com/fdaudens/pomodoro2 - App: https://github.com/fdaudens/pomodoro2/releases/tag/v1.0.0 Key takeaways: ๐Ÿš€ AI-assisted development is evolving rapidly ๐Ÿงฉ Domain expertise + AI tools can yield impressive results ๐ŸŒ This approach democratizes app creation ๐Ÿค” What's your take on AI-powered development? Have you experimented with it? #AIinDevelopment #BarefootDeveloper #OpenSource
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2024-07-26T14:46:10.000Z
2024-07-26T14:46:10.510Z
[]
/posts/fdaudens/454864775422313
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161395148988600
[ { "type": "text", "value": "Hi All, ", "raw": "Hi All, ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "In my latest blog post, I created a comprehensive guide on LLM Benchmarking. ", "raw": "In my latest blog post, I created a comprehensive guide on LLM Benchmarking. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โžŸ 20+ key benchmarks, from MMLU to TruthfulQA", "raw": "โžŸ 20+ key benchmarks, from MMLU to TruthfulQA", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โžŸ How each benchmark assesses different LLM capabilities", "raw": "โžŸ How each benchmark assesses different LLM capabilities", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โžŸ Why benchmarking matters for real-world AI applications", "raw": "โžŸ Why benchmarking matters for real-world AI applications", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โžŸ Future trends in AI evaluation", "raw": "โžŸ Future trends in AI evaluation", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Read the blog here: ", "raw": "Read the blog here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://wp.me/p7Qix-wO", "href": "https://wp.me/p7Qix-wO", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Please let me know your thoughts, suggestions, and comments. ", "raw": "Please let me know your thoughts, suggestions, and comments. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Hi All, In my latest blog post, I created a comprehensive guide on LLM Benchmarking. โžŸ 20+ key benchmarks, from MMLU to TruthfulQA โžŸ How each benchmark assesses different LLM capabilities โžŸ Why benchmarking matters for real-world AI applications โžŸ Future trends in AI evaluation Read the blog here: https://wp.me/p7Qix-wO Please let me know your thoughts, suggestions, and comments.
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2024-07-26T14:14:08.000Z
2024-07-26T14:15:11.110Z
[]
/posts/ajithprabhakar/161395148988600
515
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431447743586428
[ { "type": "text", "value": "Try to find a better int4 algorithm for LLAMA3.1? For the 8B model, AutoRound boasts an average improvement across 10 zero-shot tasks, scoring 63.93 versus 63.15 (AWQ). Notably, on the MMLU task, it achieved 66.72 compared to 65.25, and on the ARC-C task, it scored 52.13 against 50.94. For further details and comparisons, visit the leaderboard at ", "raw": "Try to find a better int4 algorithm for LLAMA3.1? For the 8B model, AutoRound boasts an average improvement across 10 zero-shot tasks, scoring 63.93 versus 63.15 (AWQ). Notably, on the MMLU task, it achieved 66.72 compared to 65.25, and on the ARC-C task, it scored 52.13 against 50.94. For further details and comparisons, visit the leaderboard at ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Intel/low_bit_open_llm_leaderboard", "href": null, "resource": { "type": "space", "id": "Intel/low_bit_open_llm_leaderboard", "discussionNum": null }, "url": "https://huggingface.co/spaces/Intel/low_bit_open_llm_leaderboard", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ".", "raw": ".", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Try to find a better int4 algorithm for LLAMA3.1? For the 8B model, AutoRound boasts an average improvement across 10 zero-shot tasks, scoring 63.93 versus 63.15 (AWQ). Notably, on the MMLU task, it achieved 66.72 compared to 65.25, and on the ARC-C task, it scored 52.13 against 50.94. For further details and comparisons, visit the leaderboard at https://huggingface.co/spaces/Intel/low_bit_open_llm_leaderboard.
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2024-07-26T08:47:10.000Z
2024-07-26T08:47:50.532Z
[]
/posts/wenhuach/431447743586428
632
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472528729644134
[ { "type": "text", "value": "When ", "raw": "When ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@MistralAI", "href": null, "resource": null, "url": null, "code": null, "user": "MistralAI", "label": null, "lang": null }, { "type": "text", "value": " drops a blog post labelled \"Large Enough,\" it's going to get serious! ๐Ÿš€๐Ÿ’ก", "raw": " drops a blog post labelled \"Large Enough,\" it's going to get serious! ๐Ÿš€๐Ÿ’ก", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Mistral-Large-Instruct-2407, just call it Mistral-Large2, is a 123B parameters Instruct model with 128k context ๐ŸŒ๐Ÿ“š", "raw": "- Mistral-Large-Instruct-2407, just call it Mistral-Large2, is a 123B parameters Instruct model with 128k context ๐ŸŒ๐Ÿ“š", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Multilingual in 11 languages; English ๐Ÿ‡ฌ๐Ÿ‡ง, French ๐Ÿ‡ซ๐Ÿ‡ท, German ๐Ÿ‡ฉ๐Ÿ‡ช, Spanish ๐Ÿ‡ช๐Ÿ‡ธ, Italian ๐Ÿ‡ฎ๐Ÿ‡น, Chinese ๐Ÿ‡จ๐Ÿ‡ณ, Japanese ๐Ÿ‡ฏ๐Ÿ‡ต, Korean ๐Ÿ‡ฐ๐Ÿ‡ท, Portuguese ๐Ÿ‡ต๐Ÿ‡น, Dutch ๐Ÿ‡ณ๐Ÿ‡ฑ, and Polish ๐Ÿ‡ต๐Ÿ‡ฑ. ๐Ÿ—ฃ๏ธ๐Ÿ—บ๏ธ", "raw": "- Multilingual in 11 languages; English ๐Ÿ‡ฌ๐Ÿ‡ง, French ๐Ÿ‡ซ๐Ÿ‡ท, German ๐Ÿ‡ฉ๐Ÿ‡ช, Spanish ๐Ÿ‡ช๐Ÿ‡ธ, Italian ๐Ÿ‡ฎ๐Ÿ‡น, Chinese ๐Ÿ‡จ๐Ÿ‡ณ, Japanese ๐Ÿ‡ฏ๐Ÿ‡ต, Korean ๐Ÿ‡ฐ๐Ÿ‡ท, Portuguese ๐Ÿ‡ต๐Ÿ‡น, Dutch ๐Ÿ‡ณ๐Ÿ‡ฑ, and Polish ๐Ÿ‡ต๐Ÿ‡ฑ. ๐Ÿ—ฃ๏ธ๐Ÿ—บ๏ธ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Also highly focused on programming, trained on 80+ coding languages such as Python, Java, C, C++, Javascript, bash ๐Ÿ’ป๐Ÿ”ง", "raw": "- Also highly focused on programming, trained on 80+ coding languages such as Python, Java, C, C++, Javascript, bash ๐Ÿ’ป๐Ÿ”ง", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Supports native function calling and structured output. ๐Ÿ› ๏ธ๐Ÿ“Š", "raw": "- Supports native function calling and structured output. ๐Ÿ› ๏ธ๐Ÿ“Š", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Released under Mistral Research License (Non-Commercial License, Research only๐Ÿ˜”) ", "raw": "- Released under Mistral Research License (Non-Commercial License, Research only๐Ÿ˜”) ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Open weights only๐Ÿ”“, no data or code released ๐Ÿ”’๐Ÿ“", "raw": "- Open weights only๐Ÿ”“, no data or code released ๐Ÿ”’๐Ÿ“", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Definitely firing shots at ", "raw": "Definitely firing shots at ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@Meta", "href": null, "resource": null, "url": null, "code": null, "user": "Meta", "label": null, "lang": null }, { "type": "text", "value": " Llama3.1: ๐ŸŽฏ๐Ÿ”ฅ", "raw": " Llama3.1: ๐ŸŽฏ๐Ÿ”ฅ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "MMLU - 84.0% (ML2) vs 79.3% (L3.1-70B) vs 85.2% (L3.1-405B)", "raw": "MMLU - 84.0% (ML2) vs 79.3% (L3.1-70B) vs 85.2% (L3.1-405B)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "GSM8K - 93% (ML2) vs 95.5% (L3.1-70B-Ins) vs 96.8% (L3.1-405B-Ins)", "raw": "GSM8K - 93% (ML2) vs 95.5% (L3.1-70B-Ins) vs 96.8% (L3.1-405B-Ins)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Also, it's kinda chunky! ๐Ÿ“ฆ๐Ÿ’ช", "raw": "Also, it's kinda chunky! ๐Ÿ“ฆ๐Ÿ’ช", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "fp16/ bf16 - ~250GB VRAM", "raw": "fp16/ bf16 - ~250GB VRAM", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "fp8/ int8 - ~125GB VRAM", "raw": "fp8/ int8 - ~125GB VRAM", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "int4 - ~60GB VRAM", "raw": "int4 - ~60GB VRAM", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I tried quantising it to AWQ and GPTQ, but couldn't with 30GB V-RAM. โŒ๐Ÿ–ฅ๏ธ", "raw": "I tried quantising it to AWQ and GPTQ, but couldn't with 30GB V-RAM. โŒ๐Ÿ–ฅ๏ธ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Also calling out AWQ and GPTQ on not supporting multi-GPU quantisation! ๐Ÿ–ฅ๏ธโšก", "raw": "Also calling out AWQ and GPTQ on not supporting multi-GPU quantisation! ๐Ÿ–ฅ๏ธโšก", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "God sent ", "raw": "God sent ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@casperhansen", "href": null, "resource": null, "url": null, "code": null, "user": "casperhansen", "label": null, "lang": null }, { "type": "text", "value": " has posted AWQ quantised INT4 model (68.68 GB) with the perplexity of 2.889: ", "raw": " has posted AWQ quantised INT4 model (68.68 GB) with the perplexity of 2.889: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/casperhansen/mistral-large-instruct-2407-awq", "href": null, "resource": { "type": "model", "id": "casperhansen/mistral-large-instruct-2407-awq", "discussionNum": null }, "url": "https://huggingface.co/casperhansen/mistral-large-instruct-2407-awq", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ๐Ÿ”ฅ๐Ÿ‘", "raw": " ๐Ÿ”ฅ๐Ÿ‘", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Looks like open AI is going to beat OpenAI! ๐Ÿ†๐Ÿค–", "raw": "Looks like open AI is going to beat OpenAI! ๐Ÿ†๐Ÿค–", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Blog post: ", "raw": "Blog post: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://mistral.ai/news/mistral-large-2407/", "href": "https://mistral.ai/news/mistral-large-2407/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Models: ", "raw": "Models: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/mistralai/Mistral-Large-Instruct-2407", "href": null, "resource": { "type": "model", "id": "mistralai/Mistral-Large-Instruct-2407", "discussionNum": null }, "url": "https://huggingface.co/mistralai/Mistral-Large-Instruct-2407", "code": null, "user": null, "label": null, "lang": null } ]
When @MistralAI drops a blog post labelled "Large Enough," it's going to get serious! ๐Ÿš€๐Ÿ’ก - Mistral-Large-Instruct-2407, just call it Mistral-Large2, is a 123B parameters Instruct model with 128k context ๐ŸŒ๐Ÿ“š - Multilingual in 11 languages; English ๐Ÿ‡ฌ๐Ÿ‡ง, French ๐Ÿ‡ซ๐Ÿ‡ท, German ๐Ÿ‡ฉ๐Ÿ‡ช, Spanish ๐Ÿ‡ช๐Ÿ‡ธ, Italian ๐Ÿ‡ฎ๐Ÿ‡น, Chinese ๐Ÿ‡จ๐Ÿ‡ณ, Japanese ๐Ÿ‡ฏ๐Ÿ‡ต, Korean ๐Ÿ‡ฐ๐Ÿ‡ท, Portuguese ๐Ÿ‡ต๐Ÿ‡น, Dutch ๐Ÿ‡ณ๐Ÿ‡ฑ, and Polish ๐Ÿ‡ต๐Ÿ‡ฑ. ๐Ÿ—ฃ๏ธ๐Ÿ—บ๏ธ - Also highly focused on programming, trained on 80+ coding languages such as Python, Java, C, C++, Javascript, bash ๐Ÿ’ป๐Ÿ”ง - Supports native function calling and structured output. ๐Ÿ› ๏ธ๐Ÿ“Š - Released under Mistral Research License (Non-Commercial License, Research only๐Ÿ˜”) - Open weights only๐Ÿ”“, no data or code released ๐Ÿ”’๐Ÿ“ Definitely firing shots at @Meta Llama3.1: ๐ŸŽฏ๐Ÿ”ฅ MMLU - 84.0% (ML2) vs 79.3% (L3.1-70B) vs 85.2% (L3.1-405B) GSM8K - 93% (ML2) vs 95.5% (L3.1-70B-Ins) vs 96.8% (L3.1-405B-Ins) Also, it's kinda chunky! ๐Ÿ“ฆ๐Ÿ’ช fp16/ bf16 - ~250GB VRAM fp8/ int8 - ~125GB VRAM int4 - ~60GB VRAM I tried quantising it to AWQ and GPTQ, but couldn't with 30GB V-RAM. โŒ๐Ÿ–ฅ๏ธ Also calling out AWQ and GPTQ on not supporting multi-GPU quantisation! ๐Ÿ–ฅ๏ธโšก God sent @casperhansen has posted AWQ quantised INT4 model (68.68 GB) with the perplexity of 2.889: https://huggingface.co/casperhansen/mistral-large-instruct-2407-awq ๐Ÿ”ฅ๐Ÿ‘ Looks like open AI is going to beat OpenAI! ๐Ÿ†๐Ÿค– Blog post: https://mistral.ai/news/mistral-large-2407/ Models: https://huggingface.co/mistralai/Mistral-Large-Instruct-2407
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[]
2024-07-26T06:43:53.000Z
2024-07-26T06:43:53.282Z
[]
/posts/singhsidhukuldeep/472528729644134
653
0
475207808661590
[ { "type": "text", "value": "Custom Enterprise LLM/RAG with Real-Time Fine-Tuning ", "raw": "Custom Enterprise LLM/RAG with Real-Time Fine-Tuning ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://mltblog.com/3WcTS9C", "href": "https://mltblog.com/3WcTS9C", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " -- Just released!", "raw": " -- Just released!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This article features an application of xLLM to extract information from a corporate corpus, using prompts referred to as โ€œqueriesโ€. The goal is to serve the business user โ€” typically an employee of the company or someone allowed access โ€” with condensed, relevant pieces of information including links, examples, PDFs, tables, charts, definitions and so on, to professional queries.", "raw": "This article features an application of xLLM to extract information from a corporate corpus, using prompts referred to as โ€œqueriesโ€. The goal is to serve the business user โ€” typically an employee of the company or someone allowed access โ€” with condensed, relevant pieces of information including links, examples, PDFs, tables, charts, definitions and so on, to professional queries.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "My custom sub-LLM designed from scratch does not rely on any Python library or API, and performs better than search tools available on the market, in terms of speed and results relevancy. It offers the user the ability to fine-tune parameters in real time, and can detect user intent to deliver appropriate output. The good performance comes from the quality of the well-structured input sources, combined with smart crawling to retrieve the embedded knowledge graph and integrate it into the backend tables. Traditional tools rely mostly on tokens, embeddings, billions of parameters and frontend tricks such as prompt engineering to fix backend issues.", "raw": "My custom sub-LLM designed from scratch does not rely on any Python library or API, and performs better than search tools available on the market, in terms of speed and results relevancy. It offers the user the ability to fine-tune parameters in real time, and can detect user intent to deliver appropriate output. The good performance comes from the quality of the well-structured input sources, combined with smart crawling to retrieve the embedded knowledge graph and integrate it into the backend tables. Traditional tools rely mostly on tokens, embeddings, billions of parameters and frontend tricks such as prompt engineering to fix backend issues.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "To the contrary, my approach focuses on building a solid backend foundational architecture from the ground up. Tokens and embeddings are not the most important components, by a long shot. Cosine similarity and dot products are replaced by pointwise mutual information. There is no neural network, no training, and a small number of explainable parameters, easy to fine-tune.", "raw": "To the contrary, my approach focuses on building a solid backend foundational architecture from the ground up. Tokens and embeddings are not the most important components, by a long shot. Cosine similarity and dot products are replaced by pointwise mutual information. There is no neural network, no training, and a small number of explainable parameters, easy to fine-tune.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Read more, access the code and data, at ", "raw": "Read more, access the code and data, at ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://mltblog.com/3WcTS9C", "href": "https://mltblog.com/3WcTS9C", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Custom Enterprise LLM/RAG with Real-Time Fine-Tuning https://mltblog.com/3WcTS9C -- Just released! This article features an application of xLLM to extract information from a corporate corpus, using prompts referred to as โ€œqueriesโ€. The goal is to serve the business user โ€” typically an employee of the company or someone allowed access โ€” with condensed, relevant pieces of information including links, examples, PDFs, tables, charts, definitions and so on, to professional queries. My custom sub-LLM designed from scratch does not rely on any Python library or API, and performs better than search tools available on the market, in terms of speed and results relevancy. It offers the user the ability to fine-tune parameters in real time, and can detect user intent to deliver appropriate output. The good performance comes from the quality of the well-structured input sources, combined with smart crawling to retrieve the embedded knowledge graph and integrate it into the backend tables. Traditional tools rely mostly on tokens, embeddings, billions of parameters and frontend tricks such as prompt engineering to fix backend issues. To the contrary, my approach focuses on building a solid backend foundational architecture from the ground up. Tokens and embeddings are not the most important components, by a long shot. Cosine similarity and dot products are replaced by pointwise mutual information. There is no neural network, no training, and a small number of explainable parameters, easy to fine-tune. Read more, access the code and data, at https://mltblog.com/3WcTS9C
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2024-07-26T00:24:16.000Z
2024-07-26T00:24:16.325Z
[]
/posts/vincentg64/475207808661590
571
0
336846441665373
[ { "type": "text", "value": "Aspen Institute's wake-up call for journalism: Embrace AI or risk obsolescence ๐Ÿ“ฐ๐Ÿค–", "raw": "Aspen Institute's wake-up call for journalism: Embrace AI or risk obsolescence ๐Ÿ“ฐ๐Ÿค–", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "\"Every new technology comes with risksโ€”it's how the media industry responds that determines how (or whether) news providers can prevail.\" โ€” ", "raw": "\"Every new technology comes with risksโ€”it's how the media industry responds that determines how (or whether) news providers can prevail.\" โ€” ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Vivian Schiller", "raw": "Vivian Schiller", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "ย ", "raw": "ย ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Bonus: In need of ideas for AI projects? The report is a goldmine of real-world experiments. Here are some lesser-known innovations:", "raw": "Bonus: In need of ideas for AI projects? The report is a goldmine of real-world experiments. Here are some lesser-known innovations:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“Š Spotting patterns: Semafor uses chatbots to assess newsroom performance", "raw": "๐Ÿ“Š Spotting patterns: Semafor uses chatbots to assess newsroom performance", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“œ Extending reach: Politico summarizes state and federal legislation with AI", "raw": "๐Ÿ“œ Extending reach: Politico summarizes state and federal legislation with AI", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐ŸŽ™๏ธ Transformation: Washington Post uses AI-generated voices for newsletter narration", "raw": "๐ŸŽ™๏ธ Transformation: Washington Post uses AI-generated voices for newsletter narration", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ€ Comprehensive coverage: Richland Source covers 10,000 Ohio high school sports games yearly with AI", "raw": "๐Ÿ€ Comprehensive coverage: Richland Source covers 10,000 Ohio high school sports games yearly with AI", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ”‘ Summarization: Gannett adds AI-generated bullet points to stories", "raw": "๐Ÿ”‘ Summarization: Gannett adds AI-generated bullet points to stories", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐ŸŒ Translation: Finnish broadcaster Yle built an AI tool to reach Ukrainian immigrants", "raw": "๐ŸŒ Translation: Finnish broadcaster Yle built an AI tool to reach Ukrainian immigrants", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐ŸŽฅ Internal info: AP's Merlin tool pinpoints key video moments", "raw": "๐ŸŽฅ Internal info: AP's Merlin tool pinpoints key video moments", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ—ณ๏ธ Civic engagement: Spotlight PA's AI assistant answers election questions", "raw": "๐Ÿ—ณ๏ธ Civic engagement: Spotlight PA's AI assistant answers election questions", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ—ฃ๏ธ Personalization: Baltimore Times customizes health news with AI voice readers", "raw": "๐Ÿ—ฃ๏ธ Personalization: Baltimore Times customizes health news with AI voice readers", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐ŸŽฏ Targeted ads: NYT's AI tool aligns content with advertisers' focus", "raw": "๐ŸŽฏ Targeted ads: NYT's AI tool aligns content with advertisers' focus", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ’ผ Conversion: WSJ uses ML to boost subscription renewals", "raw": "๐Ÿ’ผ Conversion: WSJ uses ML to boost subscription renewals", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "A must-read for all in media: ", "raw": "A must-read for all in media: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://aspendigital.org/wp-content/uploads/2024/07/Aspen-Digital_Here-Come-the-Robots_July-2024.pdf", "href": "https://aspendigital.org/wp-content/uploads/2024/07/Aspen-Digital_Here-Come-the-Robots_July-2024.pdf", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "#AIinJournalism #MediaInnovation #FutureofNews", "raw": "#AIinJournalism #MediaInnovation #FutureofNews", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Aspen Institute's wake-up call for journalism: Embrace AI or risk obsolescence ๐Ÿ“ฐ๐Ÿค– "Every new technology comes with risksโ€”it's how the media industry responds that determines how (or whether) news providers can prevail." โ€” Vivian Schiller ย  Bonus: In need of ideas for AI projects? The report is a goldmine of real-world experiments. Here are some lesser-known innovations: ๐Ÿ“Š Spotting patterns: Semafor uses chatbots to assess newsroom performance ๐Ÿ“œ Extending reach: Politico summarizes state and federal legislation with AI ๐ŸŽ™๏ธ Transformation: Washington Post uses AI-generated voices for newsletter narration ๐Ÿ€ Comprehensive coverage: Richland Source covers 10,000 Ohio high school sports games yearly with AI ๐Ÿ”‘ Summarization: Gannett adds AI-generated bullet points to stories ๐ŸŒ Translation: Finnish broadcaster Yle built an AI tool to reach Ukrainian immigrants ๐ŸŽฅ Internal info: AP's Merlin tool pinpoints key video moments ๐Ÿ—ณ๏ธ Civic engagement: Spotlight PA's AI assistant answers election questions ๐Ÿ—ฃ๏ธ Personalization: Baltimore Times customizes health news with AI voice readers ๐ŸŽฏ Targeted ads: NYT's AI tool aligns content with advertisers' focus ๐Ÿ’ผ Conversion: WSJ uses ML to boost subscription renewals A must-read for all in media: https://aspendigital.org/wp-content/uploads/2024/07/Aspen-Digital_Here-Come-the-Robots_July-2024.pdf #AIinJournalism #MediaInnovation #FutureofNews
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2024-07-25T20:01:46.000Z
2024-07-25T20:01:46.720Z
[]
/posts/fdaudens/336846441665373
593
0
852983376807703
[ { "type": "text", "value": "๐Ÿ˜ Hello from Project Fluently Team!", "raw": "๐Ÿ˜ Hello from Project Fluently Team!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœจ Finally we can give you some details about Supple Diffusion. We worked on it for a long time and we have little left, we apologize that we had to increase the work time.", "raw": "โœจ Finally we can give you some details about Supple Diffusion. We worked on it for a long time and we have little left, we apologize that we had to increase the work time.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ› ๏ธ Some technical information. The first version will be the Small version (there will also be Medium, Large, Huge, possibly Tiny), it will be based on the SD1 architecture, that is, one text encoder, U-net, VAE. Now about each component, the first is a text encoder, it will be a CLIP model (perhaps not CLIP-L-path14), CLIP was specially retrained by us in order to achieve the universality of the model in understanding completely different styles and to simplify the prompt as much as possible. Next, we did U-net, U-net in a rather complicated way, first we trained different parts (types) of data with different U-nets, then we carried out merging using different methods, then we trained DPO and SPO using methods, and then we looked at the remaining shortcomings and further trained model, details will come later. We left VAE the same as in SD1 architecture.", "raw": "๐Ÿ› ๏ธ Some technical information. The first version will be the Small version (there will also be Medium, Large, Huge, possibly Tiny), it will be based on the SD1 architecture, that is, one text encoder, U-net, VAE. Now about each component, the first is a text encoder, it will be a CLIP model (perhaps not CLIP-L-path14), CLIP was specially retrained by us in order to achieve the universality of the model in understanding completely different styles and to simplify the prompt as much as possible. Next, we did U-net, U-net in a rather complicated way, first we trained different parts (types) of data with different U-nets, then we carried out merging using different methods, then we trained DPO and SPO using methods, and then we looked at the remaining shortcomings and further trained model, details will come later. We left VAE the same as in SD1 architecture.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ™Œ Compatibility. Another goal of the Supple model series is full compatibility with Auto1111 and ComfyUI already at the release stage, the model is fully supported by these interfaces and the diffusers library and does not require adaptation, your usual Sampling methods are also compatible, such as DPM++ 2M Karras, DPM++ SDE and others.", "raw": "๐Ÿ™Œ Compatibility. Another goal of the Supple model series is full compatibility with Auto1111 and ComfyUI already at the release stage, the model is fully supported by these interfaces and the diffusers library and does not require adaptation, your usual Sampling methods are also compatible, such as DPM++ 2M Karras, DPM++ SDE and others.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿง Today, without demo images (there wasnโ€™t much time), final work is underway on the model and we are already preparing to develop the Medium version, the release of the Small version will most likely be in mid-August or earlier.", "raw": "๐Ÿง Today, without demo images (there wasnโ€™t much time), final work is underway on the model and we are already preparing to develop the Medium version, the release of the Small version will most likely be in mid-August or earlier.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ˜ป Feel free to ask your questions in the comments below the post, we will be happy to answer them, have a nice day!", "raw": "๐Ÿ˜ป Feel free to ask your questions in the comments below the post, we will be happy to answer them, have a nice day!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
๐Ÿ˜ Hello from Project Fluently Team! โœจ Finally we can give you some details about Supple Diffusion. We worked on it for a long time and we have little left, we apologize that we had to increase the work time. ๐Ÿ› ๏ธ Some technical information. The first version will be the Small version (there will also be Medium, Large, Huge, possibly Tiny), it will be based on the SD1 architecture, that is, one text encoder, U-net, VAE. Now about each component, the first is a text encoder, it will be a CLIP model (perhaps not CLIP-L-path14), CLIP was specially retrained by us in order to achieve the universality of the model in understanding completely different styles and to simplify the prompt as much as possible. Next, we did U-net, U-net in a rather complicated way, first we trained different parts (types) of data with different U-nets, then we carried out merging using different methods, then we trained DPO and SPO using methods, and then we looked at the remaining shortcomings and further trained model, details will come later. We left VAE the same as in SD1 architecture. ๐Ÿ™Œ Compatibility. Another goal of the Supple model series is full compatibility with Auto1111 and ComfyUI already at the release stage, the model is fully supported by these interfaces and the diffusers library and does not require adaptation, your usual Sampling methods are also compatible, such as DPM++ 2M Karras, DPM++ SDE and others. ๐Ÿง Today, without demo images (there wasnโ€™t much time), final work is underway on the model and we are already preparing to develop the Medium version, the release of the Small version will most likely be in mid-August or earlier. ๐Ÿ˜ป Feel free to ask your questions in the comments below the post, we will be happy to answer them, have a nice day!
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2024-07-25T19:22:16.000Z
2024-08-13T04:33:15.316Z
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/posts/ehristoforu/852983376807703
3,612
1
610480712922619
[ { "type": "text", "value": "๐—”๐—ด๐—ฒ๐—ป๐˜๐—ถ๐—ฐ ๐——๐—ฎ๐˜๐—ฎ ๐—ฎ๐—ป๐—ฎ๐—น๐˜†๐˜€๐˜: ๐—ฑ๐—ฟ๐—ผ๐—ฝ ๐˜†๐—ผ๐˜‚๐—ฟ ๐—ฑ๐—ฎ๐˜๐—ฎ ๐—ณ๐—ถ๐—น๐—ฒ, ๐—น๐—ฒ๐˜ ๐˜๐—ต๐—ฒ ๐—Ÿ๐—Ÿ๐—  ๐—ฑ๐—ผ ๐˜๐—ต๐—ฒ ๐—ฎ๐—ป๐—ฎ๐—น๐˜†๐˜€๐—ถ๐˜€ ๐Ÿ“Šโš™๏ธ", "raw": "๐—”๐—ด๐—ฒ๐—ป๐˜๐—ถ๐—ฐ ๐——๐—ฎ๐˜๐—ฎ ๐—ฎ๐—ป๐—ฎ๐—น๐˜†๐˜€๐˜: ๐—ฑ๐—ฟ๐—ผ๐—ฝ ๐˜†๐—ผ๐˜‚๐—ฟ ๐—ฑ๐—ฎ๐˜๐—ฎ ๐—ณ๐—ถ๐—น๐—ฒ, ๐—น๐—ฒ๐˜ ๐˜๐—ต๐—ฒ ๐—Ÿ๐—Ÿ๐—  ๐—ฑ๐—ผ ๐˜๐—ต๐—ฒ ๐—ฎ๐—ป๐—ฎ๐—น๐˜†๐˜€๐—ถ๐˜€ ๐Ÿ“Šโš™๏ธ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Need to make quick exploratory data analysis? โžก๏ธ Get help from an agent.", "raw": "Need to make quick exploratory data analysis? โžก๏ธ Get help from an agent.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I was impressed by Llama-3.1's capacity to derive insights from data. Given a csv file, it makes quick work of exploratory data analysis and can derive interesting insights.", "raw": "I was impressed by Llama-3.1's capacity to derive insights from data. Given a csv file, it makes quick work of exploratory data analysis and can derive interesting insights.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "On the data from the Kaggle titanic challenge, that records which passengers survived the Titanic wreckage, it was able by itself to derive interesting trends like \"passengers that paid higher fares were more likely to survive\" or \"survival rate was much higher for women than men\".", "raw": "On the data from the Kaggle titanic challenge, that records which passengers survived the Titanic wreckage, it was able by itself to derive interesting trends like \"passengers that paid higher fares were more likely to survive\" or \"survival rate was much higher for women than men\".", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The cookbook even lets the agent built its own submission to the challenge, and it ranks under 3,000 out of 17,000 submissions: ๐Ÿ‘ not bad at all!", "raw": "The cookbook even lets the agent built its own submission to the challenge, and it ranks under 3,000 out of 17,000 submissions: ๐Ÿ‘ not bad at all!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Try it for yourself in this Space demo ๐Ÿ‘‰ ", "raw": "Try it for yourself in this Space demo ๐Ÿ‘‰ ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/m-ric/agent-data-analyst", "href": null, "resource": { "type": "space", "id": "m-ric/agent-data-analyst", "discussionNum": null }, "url": "https://huggingface.co/spaces/m-ric/agent-data-analyst", "code": null, "user": null, "label": null, "lang": null } ]
๐—”๐—ด๐—ฒ๐—ป๐˜๐—ถ๐—ฐ ๐——๐—ฎ๐˜๐—ฎ ๐—ฎ๐—ป๐—ฎ๐—น๐˜†๐˜€๐˜: ๐—ฑ๐—ฟ๐—ผ๐—ฝ ๐˜†๐—ผ๐˜‚๐—ฟ ๐—ฑ๐—ฎ๐˜๐—ฎ ๐—ณ๐—ถ๐—น๐—ฒ, ๐—น๐—ฒ๐˜ ๐˜๐—ต๐—ฒ ๐—Ÿ๐—Ÿ๐—  ๐—ฑ๐—ผ ๐˜๐—ต๐—ฒ ๐—ฎ๐—ป๐—ฎ๐—น๐˜†๐˜€๐—ถ๐˜€ ๐Ÿ“Šโš™๏ธ Need to make quick exploratory data analysis? โžก๏ธ Get help from an agent. I was impressed by Llama-3.1's capacity to derive insights from data. Given a csv file, it makes quick work of exploratory data analysis and can derive interesting insights. On the data from the Kaggle titanic challenge, that records which passengers survived the Titanic wreckage, it was able by itself to derive interesting trends like "passengers that paid higher fares were more likely to survive" or "survival rate was much higher for women than men". The cookbook even lets the agent built its own submission to the challenge, and it ranks under 3,000 out of 17,000 submissions: ๐Ÿ‘ not bad at all! Try it for yourself in this Space demo ๐Ÿ‘‰ https://huggingface.co/spaces/m-ric/agent-data-analyst
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2024-07-25T16:57:12.000Z
2024-07-30T03:48:40.769Z
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/posts/m-ric/610480712922619
2,266
2
602595245870297
[ { "type": "text", "value": "In principle, it's possible to \"abliterate\" refusals in any Llama 3.1 8B models via application of a LoRA, using only mergekit.", "raw": "In principle, it's possible to \"abliterate\" refusals in any Llama 3.1 8B models via application of a LoRA, using only mergekit.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Proof of concept below:", "raw": "Proof of concept below:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter", "href": null, "resource": { "type": "model", "id": "grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter", "discussionNum": null }, "url": "https://huggingface.co/grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter", "code": null, "user": null, "label": null, "lang": null } ]
In principle, it's possible to "abliterate" refusals in any Llama 3.1 8B models via application of a LoRA, using only mergekit. Proof of concept below: https://huggingface.co/grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter
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2024-07-25T16:53:54.000Z
2024-07-26T15:52:48.766Z
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/posts/grimjim/602595245870297
2,285
5
422078687475543
[ { "type": "text", "value": "Made a demo for all my Brazil XL LoRA models so far. Use it for free at ", "raw": "Made a demo for all my Brazil XL LoRA models so far. Use it for free at ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/lucianosb/brazilxl-demo", "href": null, "resource": { "type": "space", "id": "lucianosb/brazilxl-demo", "discussionNum": null }, "url": "https://huggingface.co/spaces/lucianosb/brazilxl-demo", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Brazil XL is an initiative that brings better representations of Brazilian culture to Stable Diffusion. I started this when I noticed some keywords would not generate the desired subject on any base model, so I trained my own models and I'm sharing them with the HF community.", "raw": "Brazil XL is an initiative that brings better representations of Brazilian culture to Stable Diffusion. I started this when I noticed some keywords would not generate the desired subject on any base model, so I trained my own models and I'm sharing them with the HF community.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I'll keep updating the space as new models get trained on the following months.", "raw": "I'll keep updating the space as new models get trained on the following months.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Made a demo for all my Brazil XL LoRA models so far. Use it for free at https://huggingface.co/spaces/lucianosb/brazilxl-demo Brazil XL is an initiative that brings better representations of Brazilian culture to Stable Diffusion. I started this when I noticed some keywords would not generate the desired subject on any base model, so I trained my own models and I'm sharing them with the HF community. I'll keep updating the space as new models get trained on the following months.
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2024-07-25T11:38:21.000Z
2024-07-25T11:38:21.544Z
[]
/posts/lucianosb/422078687475543
1,296
0
660573758994799
[ { "type": "text", "value": "We have recently merged Video-LLaVA to transformers! ๐Ÿค—๐ŸŽž๏ธ", "raw": "We have recently merged Video-LLaVA to transformers! ๐Ÿค—๐ŸŽž๏ธ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "What makes this model different?", "raw": "What makes this model different?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Demo: ", "raw": "Demo: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/llava-hf/video-llava", "href": null, "resource": { "type": "space", "id": "llava-hf/video-llava", "discussionNum": null }, "url": "https://huggingface.co/spaces/llava-hf/video-llava", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Model: ", "raw": "Model: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/LanguageBind/Video-LLaVA-7B-hf", "href": null, "resource": { "type": "model", "id": "LanguageBind/Video-LLaVA-7B-hf", "discussionNum": null }, "url": "https://huggingface.co/LanguageBind/Video-LLaVA-7B-hf", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Compared to other models that take image and video input and either project them separately or downsampling video and projecting selected frames, Video-LLaVA is converting images and videos to unified representation and project them using a shared projection layer.", "raw": "Compared to other models that take image and video input and either project them separately or downsampling video and projecting selected frames, Video-LLaVA is converting images and videos to unified representation and project them using a shared projection layer.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It uses Vicuna 1.5 as the language model and LanguageBind's own encoders that's based on OpenCLIP, these encoders project the modalities to an unified representation before passing to projection layer.", "raw": "It uses Vicuna 1.5 as the language model and LanguageBind's own encoders that's based on OpenCLIP, these encoders project the modalities to an unified representation before passing to projection layer.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I feel like one of the coolest features of this model is the joint understanding which is also introduced recently with many models", "raw": "I feel like one of the coolest features of this model is the joint understanding which is also introduced recently with many models", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It's a relatively older model but ahead of it's time and works very well! Which means, e.g. you can pass model an image of a cat and a video of a cat and ask questions like whether the cat in the image exists in video or not ๐Ÿคฉ", "raw": "It's a relatively older model but ahead of it's time and works very well! Which means, e.g. you can pass model an image of a cat and a video of a cat and ask questions like whether the cat in the image exists in video or not ๐Ÿคฉ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
We have recently merged Video-LLaVA to transformers! ๐Ÿค—๐ŸŽž๏ธ What makes this model different? Demo: https://huggingface.co/spaces/llava-hf/video-llava Model: https://huggingface.co/LanguageBind/Video-LLaVA-7B-hf Compared to other models that take image and video input and either project them separately or downsampling video and projecting selected frames, Video-LLaVA is converting images and videos to unified representation and project them using a shared projection layer. It uses Vicuna 1.5 as the language model and LanguageBind's own encoders that's based on OpenCLIP, these encoders project the modalities to an unified representation before passing to projection layer. I feel like one of the coolest features of this model is the joint understanding which is also introduced recently with many models It's a relatively older model but ahead of it's time and works very well! Which means, e.g. you can pass model an image of a cat and a video of a cat and ask questions like whether the cat in the image exists in video or not ๐Ÿคฉ
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2024-07-25T11:03:44.000Z
2024-07-25T11:03:44.465Z
[]
/posts/merve/660573758994799
2,267
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794863533092905
[ { "type": "text", "value": "The Meta Llama-3.1 model series can be used for distilling and fine-tuning but this requires annotated preference data so I created a Human Feedback Collector based on Gradio that directly logs data to the Hugging Face Hub. ", "raw": "The Meta Llama-3.1 model series can be used for distilling and fine-tuning but this requires annotated preference data so I created a Human Feedback Collector based on Gradio that directly logs data to the Hugging Face Hub. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Model meta-llama/Meta-Llama-3.1-8B-Instruct", "raw": "- Model meta-llama/Meta-Llama-3.1-8B-Instruct", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Data SFT, KTO and DPO data", "raw": "- Data SFT, KTO and DPO data", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Runs on free Zero GPUs in Hugging Face Spaces", "raw": "- Runs on free Zero GPUs in Hugging Face Spaces", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Might need some human curation in Argilla", "raw": "- Might need some human curation in Argilla", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Or provide some AI feedback with distilabel", "raw": "- Or provide some AI feedback with distilabel", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/collections/davidberenstein1957/chatinterface-llm-human-feedback-collectors-66a22859c9e703d2af7500c1", "href": "https://huggingface.co/collections/davidberenstein1957/chatinterface-llm-human-feedback-collectors-66a22859c9e703d2af7500c1", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
The Meta Llama-3.1 model series can be used for distilling and fine-tuning but this requires annotated preference data so I created a Human Feedback Collector based on Gradio that directly logs data to the Hugging Face Hub. - Model meta-llama/Meta-Llama-3.1-8B-Instruct - Data SFT, KTO and DPO data - Runs on free Zero GPUs in Hugging Face Spaces - Might need some human curation in Argilla - Or provide some AI feedback with distilabel https://huggingface.co/collections/davidberenstein1957/chatinterface-llm-human-feedback-collectors-66a22859c9e703d2af7500c1
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2024-07-25T10:28:22.000Z
2024-07-25T10:28:22.239Z
[]
/posts/davidberenstein1957/794863533092905
1,409
0
191397440129338
[ { "type": "text", "value": "Bellman Swedish finetune based on llama3.1 8b is now available:", "raw": "Bellman Swedish finetune based on llama3.1 8b is now available:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/neph1/llama-3.1-instruct-bellman-8b-swedish", "href": null, "resource": { "type": "model", "id": "neph1/llama-3.1-instruct-bellman-8b-swedish", "discussionNum": null }, "url": "https://huggingface.co/neph1/llama-3.1-instruct-bellman-8b-swedish", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "More quants and fp16 are coming. Working out some issues with colab..", "raw": "More quants and fp16 are coming. Working out some issues with colab..", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Bellman Swedish finetune based on llama3.1 8b is now available: https://huggingface.co/neph1/llama-3.1-instruct-bellman-8b-swedish More quants and fp16 are coming. Working out some issues with colab..
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2024-07-25T06:53:31.000Z
2024-07-25T06:53:31.693Z
[]
/posts/neph1/191397440129338
739
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750213477785569
[ { "type": "text", "value": "Super Exciting New Paper By Meta๐Ÿค–๐Ÿง ๐Ÿš€", "raw": "Super Exciting New Paper By Meta๐Ÿค–๐Ÿง ๐Ÿš€", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Discrete Flow Matching:", "raw": "Discrete Flow Matching:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Introduces a new framework/algorithm for generating text/code without having to predict auto-regressively or one โ€œwordโ€ at a time as traditional GPT models do. It generates all parts of the text/code at once.", "raw": "Introduces a new framework/algorithm for generating text/code without having to predict auto-regressively or one โ€œwordโ€ at a time as traditional GPT models do. It generates all parts of the text/code at once.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The algorithm does this by slowly transforming random noise (source) into meaningful text (data). It learns how to transform samples along a path created between source and target using a \"probability velocity\" that describes how probabilities change over time. During generation, DFM starts with a random sample and iteratively updates it using this learned velocity, gradually transforming it into a sample from the target distribution. This allows for non-autoregressive generation.", "raw": "The algorithm does this by slowly transforming random noise (source) into meaningful text (data). It learns how to transform samples along a path created between source and target using a \"probability velocity\" that describes how probabilities change over time. During generation, DFM starts with a random sample and iteratively updates it using this learned velocity, gradually transforming it into a sample from the target distribution. This allows for non-autoregressive generation.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "They were able to scale models of up to 1.7B parameters achieving impressive scores on HumanEval and MBPP for coding, significantly closing the gap between autoregressive models and discrete flow models.", "raw": "They were able to scale models of up to 1.7B parameters achieving impressive scores on HumanEval and MBPP for coding, significantly closing the gap between autoregressive models and discrete flow models.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Though in its infancy, it sure does hold a promising future as leading research scientists argue non-autoregressive methods yield better reasoning.", "raw": "Though in its infancy, it sure does hold a promising future as leading research scientists argue non-autoregressive methods yield better reasoning.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Super Exciting New Paper By Meta๐Ÿค–๐Ÿง ๐Ÿš€ Discrete Flow Matching: Introduces a new framework/algorithm for generating text/code without having to predict auto-regressively or one โ€œwordโ€ at a time as traditional GPT models do. It generates all parts of the text/code at once. The algorithm does this by slowly transforming random noise (source) into meaningful text (data). It learns how to transform samples along a path created between source and target using a "probability velocity" that describes how probabilities change over time. During generation, DFM starts with a random sample and iteratively updates it using this learned velocity, gradually transforming it into a sample from the target distribution. This allows for non-autoregressive generation. They were able to scale models of up to 1.7B parameters achieving impressive scores on HumanEval and MBPP for coding, significantly closing the gap between autoregressive models and discrete flow models. Though in its infancy, it sure does hold a promising future as leading research scientists argue non-autoregressive methods yield better reasoning.
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2024-07-25T06:36:56.000Z
2024-07-25T06:36:56.835Z
[]
/posts/Jaward/750213477785569
1,718
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909191721549783
[ { "type": "text", "value": "Yet another post hailing how good Meta Llama 3.1 is? ๐Ÿค” I guess not!", "raw": "Yet another post hailing how good Meta Llama 3.1 is? ๐Ÿค” I guess not!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "While Llama 3.1 is truly impressive, especially 405B (which gives GPT-4o a run for its money! ๐Ÿ’ช)", "raw": "While Llama 3.1 is truly impressive, especially 405B (which gives GPT-4o a run for its money! ๐Ÿ’ช)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I was surprised to see that on the Open LLM Leaderboard, Llama 3.1 70B was not able to dethrone the current king Qwen2-72B! ๐Ÿ‘‘", "raw": "I was surprised to see that on the Open LLM Leaderboard, Llama 3.1 70B was not able to dethrone the current king Qwen2-72B! ๐Ÿ‘‘", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Not only that, for a few benchmarks like MATH Lvl 5, it was completely lagging behind Qwen2-72B! ๐Ÿ“‰", "raw": "Not only that, for a few benchmarks like MATH Lvl 5, it was completely lagging behind Qwen2-72B! ๐Ÿ“‰", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Also, the benchmarks are completely off compared to the official numbers from Meta! ๐Ÿคฏ", "raw": "Also, the benchmarks are completely off compared to the official numbers from Meta! ๐Ÿคฏ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Based on the responses, I still believe Llama 3.1 will perform better than Qwen2 on LMSYS Chatbot Arena. ๐Ÿค– But it still lags behind on too many benchmarks! ๐Ÿƒโ€โ™‚๏ธ", "raw": "Based on the responses, I still believe Llama 3.1 will perform better than Qwen2 on LMSYS Chatbot Arena. ๐Ÿค– But it still lags behind on too many benchmarks! ๐Ÿƒโ€โ™‚๏ธ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Open LLM Leaderboard: ", "raw": "Open LLM Leaderboard: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard", "href": null, "resource": { "type": "space", "id": "open-llm-leaderboard/open_llm_leaderboard", "discussionNum": null }, "url": "https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ๐ŸŒ", "raw": " ๐ŸŒ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Hopefully, this is just an Open LLM Leaderboard error! ", "raw": "Hopefully, this is just an Open LLM Leaderboard error! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@open-llm-leaderboard", "href": null, "resource": null, "url": null, "code": null, "user": "open-llm-leaderboard", "label": null, "lang": null }, { "type": "text", "value": " SOS! ๐Ÿšจ", "raw": " SOS! ๐Ÿšจ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Yet another post hailing how good Meta Llama 3.1 is? ๐Ÿค” I guess not! While Llama 3.1 is truly impressive, especially 405B (which gives GPT-4o a run for its money! ๐Ÿ’ช) I was surprised to see that on the Open LLM Leaderboard, Llama 3.1 70B was not able to dethrone the current king Qwen2-72B! ๐Ÿ‘‘ Not only that, for a few benchmarks like MATH Lvl 5, it was completely lagging behind Qwen2-72B! ๐Ÿ“‰ Also, the benchmarks are completely off compared to the official numbers from Meta! ๐Ÿคฏ Based on the responses, I still believe Llama 3.1 will perform better than Qwen2 on LMSYS Chatbot Arena. ๐Ÿค– But it still lags behind on too many benchmarks! ๐Ÿƒโ€โ™‚๏ธ Open LLM Leaderboard: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard ๐ŸŒ Hopefully, this is just an Open LLM Leaderboard error! @open-llm-leaderboard SOS! ๐Ÿšจ
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2024-07-25T06:20:26.000Z
2024-07-26T17:47:27.707Z
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/posts/singhsidhukuldeep/909191721549783
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