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] | ๐ 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!
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] | ๐๐ปโโ๏ธHey there folks ,
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] | Black Forest Labs, BASED! ๐
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. ๐
Update ๐คOfficial demo:
https://huggingface.co/spaces/black-forest-labs/FLUX.1-dev
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] | 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 ๐
https://huggingface.co/datasets/argilla/magpie-ultra-v0.1
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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
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"value": "๐ 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)",
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"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! โก๏ธ As a result, existing video segmentation datasets have a real lack of coverage: few examples, few masklets drawn.",
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"value": "โ To solve this, they build a data generation system that they scale up progressively in 3 successive manual annotations phases:",
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"value": "๐ฆ๐๐ฒ๐ฝ ๐ญ: Annotators use only SAM + manual editing tools on each frame โ Create 16k masklets across 1.4k videos",
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"value": "๐ฆ๐๐ฒ๐ฝ ๐ฎ: 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! ๐ Collect 60k masklets",
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"value": "๐ฆ๐๐ฒ๐ฝ ๐ฏ: Now SAM 2 is more powerful, it has the โsingle clickโ prompting option, thus annotators can use it with simple clicks to re-annotate data.",
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] | ๐ฆ๐๐ ๐ฎ ๐ฟ๐ฒ๐น๐ฒ๐ฎ๐๐ฒ๐ฑ: ๐ก๐ฒ๐ ๐ฆ๐ข๐ง๐ ๐ผ๐ป ๐๐ฒ๐ด๐บ๐ฒ๐ป๐๐ฎ๐๐ถ๐ผ๐ป, ๐ฏ๐ ๐ฐ๐ผ๐บ๐ฏ๐ถ๐ป๐ถ๐ป๐ด ๐๐๐ป๐๐ต๐ฒ๐๐ถ๐ฐ ๐ฑ๐ฎ๐๐ฎ ๐๐ถ๐๐ต ๐ต๐๐บ๐ฎ๐ป ๐ณ๐ฒ๐ฒ๐ฑ๐ฏ๐ฎ๐ฐ๐ธ ๐
It's a model for Object segmentation, for both image and video:
๐ input = a text prompt, or a click on a specific object
๐ 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)
๐ช 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! โก๏ธ As a result, existing video segmentation datasets have a real lack of coverage: few examples, few masklets drawn.
๐ก Key idea: researchers they decided to use a segmentation model to help them collect the dataset.
But then itโs a chicken and egg problem: you need the model to create the dataset and the opposite as well? ๐ค
โ To solve this, they build a data generation system that they scale up progressively in 3 successive manual annotations phases:
๐ฆ๐๐ฒ๐ฝ ๐ญ: Annotators use only SAM + manual editing tools on each frame โ Create 16k masklets across 1.4k videos
๐ฆ๐๐ฒ๐ฝ ๐ฎ: 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! ๐ Collect 60k masklets
๐ฆ๐๐ฒ๐ฝ ๐ฏ: Now SAM 2 is more powerful, it has the โsingle 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 ๐ | {
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] | Running Gemma 2 2B at 41.66 tokens/s on my MacBook ๐ป๐
- MLX Community's swift conversion
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- Small yet powerful on-device model
Try it yourself: https://huggingface.co/collections/mlx-community/google-gemma2-667dca89bc9abbfa34080066
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This small revolution includes:
๐ย You can now integrate with the Hugging Face Hub and get started in under five minutes.
๐ชย A single `Dataset` class is now designed to handle multiple tasks.
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๐ย A new feature automates splitting annotation tasks among a team.
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"value": "๐๐น๐ฎ๐บ๐ฎ-๐ฏ.๐ญ ๐บ๐ผ๐ฑ๐ฒ๐น๐ ๐ณ๐ถ๐ป๐ฎ๐น๐น๐ ๐ด๐ฒ๐ ๐๐ต๐ฒ๐ถ๐ฟ ๐๐ต๐ฎ๐๐ฏ๐ผ๐ ๐๐ฟ๐ฒ๐ป๐ฎ ๐ฟ๐ฎ๐ป๐ธ๐ถ๐ป๐ด ๐๏ธ",
"raw": "๐๐น๐ฎ๐บ๐ฎ-๐ฏ.๐ญ ๐บ๐ผ๐ฑ๐ฒ๐น๐ ๐ณ๐ถ๐ป๐ฎ๐น๐น๐ ๐ด๐ฒ๐ ๐๐ต๐ฒ๐ถ๐ฟ ๐๐ต๐ฎ๐๐ฏ๐ผ๐ ๐๐ฟ๐ฒ๐ป๐ฎ ๐ฟ๐ฎ๐ป๐ธ๐ถ๐ป๐ด ๐๏ธ",
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"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.",
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"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:",
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"value": "๐ฅ 405B Model ranks 5th overall, in front of GPT-4-turbo! But behind GPT-4o, Claude-3.5 Sonnet and Gemini-advanced.",
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"value": "๐ 70B Model climbs up to 9th rank ! From 1206 โก๏ธ 1244.",
"raw": "๐ 70B Model climbs up to 9th rank ! From 1206 โก๏ธ 1244.",
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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!",
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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!",
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"value": "โค GPT-4-Turbo inference price from OpenAI: $5/M input tokens, $15/M output tokens",
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"value": "โค Llama-3.1-405B from HF API (for testing only): 3$/M for input or output tokens (Source linked in the first comment)",
"raw": "โค Llama-3.1-405B from HF API (for testing only): 3$/M for input or output tokens (Source linked in the first comment)",
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] | ๐๐น๐ฎ๐บ๐ฎ-๐ฏ.๐ญ ๐บ๐ผ๐ฑ๐ฒ๐น๐ ๐ณ๐ถ๐ป๐ฎ๐น๐น๐ ๐ด๐ฒ๐ ๐๐ต๐ฒ๐ถ๐ฟ ๐๐ต๐ฎ๐๐ฏ๐ผ๐ ๐๐ฟ๐ฒ๐ป๐ฎ ๐ฟ๐ฎ๐ป๐ธ๐ถ๐ป๐ด ๐๏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:
๐ฅ 405B Model ranks 5th overall, in front of GPT-4-turbo! But behind GPT-4o, Claude-3.5 Sonnet and Gemini-advanced.
๐ 70B Model climbs up to 9th rank ! From 1206 โก๏ธ 1244.
๐ 8B Model improves from 1152 โก๏ธ 1170.
โ
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;
โค GPT-4-Turbo inference price from OpenAI: $5/M input tokens, $15/M output tokens
โค Llama-3.1-405B from HF API (for testing only): 3$/M for input or output tokens (Source linked in the first comment)
โค Llama-3.1-405B from HF API (for testing only): free โจ
Get a head start on the HF API (resource by @andrewrreed) ๐ https://huggingface.co/learn/cookbook/enterprise_hub_serverless_inference_api | {
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```python
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"raw": "๐ข 8k and 32k versions available",
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"value": "๐จโโ๏ธ Great for diagnosing, planning treatments, medical research, insurance coding and billing",
"raw": "๐จโโ๏ธ Great for diagnosing, planning treatments, medical research, insurance coding and billing",
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] | ๐ฅ 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
๐ข 8k and 32k versions available
๐ MMLU performance of ~86%, outperforming other top models
๐จโโ๏ธ Great for diagnosing, planning treatments, medical research, insurance coding and billing
๐ Open-model license for non-commercial use cases
๐ค Available on Hugging Face: https://huggingface.co/Writer/Palmyra-Med-70B
๐พ Live on NVIDIA NIM: https://build.nvidia.com/writer/palmyra-med-70b
Palmyra-Fin-70b
๐ Passed the CFA Level III exam with a 73% score โ the first model to do so
๐ธ Skilled at complex tasks like investment research, financial analysis, and sentiment analysis
๐ Outperformed other top models on a long-fin-eval test of real-world use cases
๐ Open-model license for non-commercial use cases
๐ค Available on Hugging Face: https://huggingface.co/Writer/Palmyra-Fin-70B-32K
๐พ 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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"raw": "๐ 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.",
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] | 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:
๐ LLMs could make 83% of reporter tasks and 76% of editor tasks way more efficient. "Itโs important to emphasize that these figures are fundamentally about augmentation rather than automation".
๐งฉ Four emerging job clusters to consider: AI-doers, AI-users, AI-strategizers, and AI-reporters. Where do you fit?
๐ 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.
๐ Key takeaway: upskill in AI. It's not just about using tools, but understanding how to integrate them into your workflow.
๐ 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?
๐ 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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] | ๐ง๐ต๐ฒ ๐ต๐๐ด๐ฒ ๐ฐ๐ผ๐๐ ๐ผ๐ณ ๐ฟ๐ฒ๐๐ฒ๐ฎ๐ฟ๐ฐ๐ต ๐ผ๐ป ๐ณ๐ฟ๐ผ๐ป๐๐ถ๐ฒ๐ฟ ๐๐๐ ๐ ๐ธ
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.
๐ฌ 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.
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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!
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We present our findings from a series of experiments on quantizing different diffusion pipelines based on diffusion transformers.
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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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] | 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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Question for the Community:
Which models should I use to generate images and audio samples for those datasets ? ๐ค | {
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"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```",
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```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()
```
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"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. ",
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] | 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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] | lamagenius
Smart free open source AI powered search engine
https://hf.co/chat/assistant/66a5fc9f02b1826ba4cabd72 | {
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] | Hi HF Community!๐ค
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.cpp๐ฆ 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!๐ฑ | {
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```
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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] | โ๏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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] | Hello, HuggingFace๐ค community ๐,
All the amazing people quantising LLMs to AWQ and GPTQ ๐ง๐ค
Can you please mention the perplexity you achieved ๐ OR any other metric to measure the quantisation qualitatively? ๐
The GGUF community follows this really well! ๐
And if it is not too much to ask, the script used for quantisation would be amazing! ๐
Thanks for the quants for the GPU poor! ๐ป | {
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] | 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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https://huggingface.co/blog/rwightman/mobilenet-baselines
https://huggingface.co/timm/mobilenetv1_100.ra4_e3600_r224_in1k
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"value": "- Data analyst agent: get your dataโs insights in the blink of an eye โจ: great recipe by our own ",
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] | At Hugging Face we have an open-source Cookbook with many applied AI recipes ๐
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 dataโs insights in the blink of an eye โจ: great recipe by our own @m-ric showing how to build an agent that can do data analysis! ๐ฑ https://huggingface.co/learn/cookbook/agent_data_analyst
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โฒ๏ธ The challenge: Create a menu bar Pomodoro app for my computer to boost my focus. Previous attempt? Messy. ",
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โฒ๏ธ The challenge: Create a menu bar Pomodoro app for my computer to boost my focus. Previous attempt? Messy. ",
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] | Barefoot developer experiment: AI-powered app creation with my poor coding skills ๐ง ๐ป
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. ๐ฌ
๐
โฒ๏ธ The challenge: Create a menu bar Pomodoro app for my computer to boost my focus. Previous attempt? Messy.
๐คฏ The twist: I've never coded in Swift.
โก๏ธ The result: 30 minutes. No joke. Elegant, functional, and shareable.
๐ 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:
๐ AI-assisted development is evolving rapidly
๐งฉ Domain expertise + AI tools can yield impressive results
๐ This approach democratizes app creation
๐ค What's your take on AI-powered development? Have you experimented with it?
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] | Hi All,
In my latest blog post, I created a comprehensive guide on LLM Benchmarking.
โ 20+ key benchmarks, from MMLU to TruthfulQA
โ How each benchmark assesses different LLM capabilities
โ Why benchmarking matters for real-world AI applications
โ 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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] | 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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"value": "I tried quantising it to AWQ and GPTQ, but couldn't with 30GB V-RAM. โ๐ฅ๏ธ",
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] | When @MistralAI drops a blog post labelled "Large Enough," it's going to get serious! ๐๐ก
- Mistral-Large-Instruct-2407, just call it Mistral-Large2, is a 123B parameters Instruct model with 128k context ๐๐
- Multilingual in 11 languages; English ๐ฌ๐ง, French ๐ซ๐ท, German ๐ฉ๐ช, Spanish ๐ช๐ธ, Italian ๐ฎ๐น, Chinese ๐จ๐ณ, Japanese ๐ฏ๐ต, Korean ๐ฐ๐ท, Portuguese ๐ต๐น, Dutch ๐ณ๐ฑ, and Polish ๐ต๐ฑ. ๐ฃ๏ธ๐บ๏ธ
- Also highly focused on programming, trained on 80+ coding languages such as Python, Java, C, C++, Javascript, bash ๐ป๐ง
- Supports native function calling and structured output. ๐ ๏ธ๐
- Released under Mistral Research License (Non-Commercial License, Research only๐)
- Open weights only๐, no data or code released ๐๐
Definitely firing shots at @Meta Llama3.1: ๐ฏ๐ฅ
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! ๐ฆ๐ช
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. โ๐ฅ๏ธ
Also calling out AWQ and GPTQ on not supporting multi-GPU quantisation! ๐ฅ๏ธโก
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 ๐ฅ๐
Looks like open AI is going to beat OpenAI! ๐๐ค
Blog post: https://mistral.ai/news/mistral-large-2407/
Models: https://huggingface.co/mistralai/Mistral-Large-Instruct-2407 | {
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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.
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"raw": "๐ Translation: Finnish broadcaster Yle built an AI tool to reach Ukrainian immigrants",
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] | Aspen Institute's wake-up call for journalism: Embrace AI or risk obsolescence ๐ฐ๐ค
"Every new technology comes with risksโit'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:
๐ Spotting patterns: Semafor uses chatbots to assess newsroom performance
๐ Extending reach: Politico summarizes state and federal legislation with AI
๐๏ธ Transformation: Washington Post uses AI-generated voices for newsletter narration
๐ Comprehensive coverage: Richland Source covers 10,000 Ohio high school sports games yearly with AI
๐ Summarization: Gannett adds AI-generated bullet points to stories
๐ Translation: Finnish broadcaster Yle built an AI tool to reach Ukrainian immigrants
๐ฅ Internal info: AP's Merlin tool pinpoints key video moments
๐ณ๏ธ Civic engagement: Spotlight PA's AI assistant answers election questions
๐ฃ๏ธ Personalization: Baltimore Times customizes health news with AI voice readers
๐ฏ Targeted ads: NYT's AI tool aligns content with advertisers' focus
๐ผ 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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"value": "โจ 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": "โจ 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.",
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"value": "๐ ๏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": "๐ ๏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.",
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"value": "๐ 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": "๐ 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.",
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"value": "๐ง Today, without demo images (there wasnโt 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": "๐ง Today, without demo images (there wasnโt 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.",
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"value": "๐ป Feel free to ask your questions in the comments below the post, we will be happy to answer them, have a nice day!",
"raw": "๐ป 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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] | ๐ Hello from Project Fluently Team!
โจ 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.
๐ ๏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.
๐ 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.
๐ง Today, without demo images (there wasnโt 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.
๐ป 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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"raw": "Need to make quick exploratory data analysis? โก๏ธ Get help from an agent.",
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Need to make quick exploratory data analysis? โก๏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: ๐ not bad at all!
Try it for yourself in this Space demo ๐ https://huggingface.co/spaces/m-ric/agent-data-analyst | {
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] | 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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] | We have recently merged Video-LLaVA to transformers! ๐ค๐๏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 ๐คฉ
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"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.",
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] | Super Exciting New Paper By Meta๐ค๐ง ๐
Discrete Flow Matching:
Introduces a new framework/algorithm for generating text/code without having to predict auto-regressively or one โwordโ 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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"value": "While Llama 3.1 is truly impressive, especially 405B (which gives GPT-4o a run for its money! ๐ช)",
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"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! ๐",
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"value": "Not only that, for a few benchmarks like MATH Lvl 5, it was completely lagging behind Qwen2-72B! ๐",
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"value": "Also, the benchmarks are completely off compared to the official numbers from Meta! ๐คฏ",
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"value": "Based on the responses, I still believe Llama 3.1 will perform better than Qwen2 on LMSYS Chatbot Arena. ๐ค But it still lags behind on too many benchmarks! ๐โโ๏ธ",
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] | Yet another post hailing how good Meta Llama 3.1 is? ๐ค I guess not!
While Llama 3.1 is truly impressive, especially 405B (which gives GPT-4o a run for its money! ๐ช)
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! ๐
Not only that, for a few benchmarks like MATH Lvl 5, it was completely lagging behind Qwen2-72B! ๐
Also, the benchmarks are completely off compared to the official numbers from Meta! ๐คฏ
Based on the responses, I still believe Llama 3.1 will perform better than Qwen2 on LMSYS Chatbot Arena. ๐ค But it still lags behind on too many benchmarks! ๐โโ๏ธ
Open LLM Leaderboard: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard ๐
Hopefully, this is just an Open LLM Leaderboard error! @open-llm-leaderboard SOS! ๐จ | {
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