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merveย 
posted an update 3 days ago
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3023
This week in open AI was ๐Ÿ”ฅ Let's recap! ๐Ÿค— merve/january-31-releases-679a10669bd4030090c5de4d
LLMs ๐Ÿ’ฌ
> Huge: AllenAI released new Tรผlu models that outperform DeepSeek R1 using Reinforcement Learning with Verifiable Reward (RLVR) based on Llama 3.1 405B ๐Ÿ”ฅ
> Mistral AI is back to open-source with their "small" 24B models (base & SFT), with Apache 2.0 license ๐Ÿ˜ฑ
> Alibaba Qwen released their 1M context length models Qwen2.5-Instruct-1M, great for agentic use with Apache 2.0 license ๐Ÿ”ฅ
> Arcee AI released Virtuoso-medium, 32.8B LLMs distilled from DeepSeek V3 with dataset of 5B+ tokens
> Velvet-14B is a new family of 14B Italian LLMs trained on 10T tokens in six languages
> OpenThinker-7B is fine-tuned version of Qwen2.5-7B-Instruct on OpenThoughts dataset

VLMs & vision ๐Ÿ‘€
> Alibaba Qwen is back with Qwen2.5VL, amazing new capabilities ranging from agentic computer use to zero-shot localization ๐Ÿ”ฅ
> NVIDIA released new series of Eagle2 models with 1B and 9B sizes
> DeepSeek released Janus-Pro, new any-to-any model (image-text generation from image-text input) with MIT license
> BEN2 is a new background removal model with MIT license!

Audio ๐Ÿ—ฃ๏ธ
> YuE is a new open-source music generation foundation model, lyrics-to-song generation

Codebase ๐Ÿ‘ฉ๐Ÿปโ€๐Ÿ’ป
> We are open-sourcing our SmolVLM training and eval codebase! https://github.com/huggingface/smollm/tree/main/vision
> Open-R1 is open-source reproduction of R1 by @huggingface science team https://huggingface.co/blog/open-r1
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m-ricย 
posted an update 3 days ago
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1709
Now you can launch a code agent directly from your terminal!
โœจ ๐šœ๐š–๐š˜๐š•๐šŠ๐š๐šŽ๐š—๐š "๐šˆ๐š˜๐šž๐š› ๐š๐šŠ๐šœ๐š”" directly launches a CodeAgent
โ–ถ๏ธ This also works with web agents (replace ๐šœ๐š–๐š˜๐š•๐šŠ๐š๐šŽ๐š—๐š with ๐š ๐šŽ๐š‹๐šŠ๐š๐šŽ๐š—๐š) thanks to @merve !

๐Ÿ’พ Another treat from smolagents release 1.7.0:
Now agents have a memory mechanism, enabling many possibilities like replaying the last run with ๐šŠ๐š๐šŽ๐š—๐š.๐š›๐šŽ๐š™๐š•๐šŠ๐šข(), thank you @clefourrier !

Check the release notes here ๐Ÿ‘‰ https://github.com/huggingface/smolagents/releases/tag/v1.7.0
davanstrienย 
posted an update 5 days ago
dylanebertย 
posted an update 5 days ago
m-ricย 
posted an update 6 days ago
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3304
๐—ง๐—ต๐—ฒ ๐—›๐˜‚๐—ฏ ๐˜„๐—ฒ๐—น๐—ฐ๐—ผ๐—บ๐—ฒ๐˜€ ๐—ฒ๐˜…๐˜๐—ฒ๐—ฟ๐—ป๐—ฎ๐—น ๐—ถ๐—ป๐—ณ๐—ฒ๐—ฟ๐—ฒ๐—ป๐—ฐ๐—ฒ ๐—ฝ๐—ฟ๐—ผ๐˜ƒ๐—ถ๐—ฑ๐—ฒ๐—ฟ๐˜€!

โœ… Hosting our own inference was not enough: now the Hub 4 new inference providers: fal, Replicate, SambaNova Systems, & Together AI.

Check model cards on the Hub: you can now, in 1 click, use inference from various providers (cf video demo)

Their inference can also be used through our Inference API client. There, you can use either your custom provider key, or your HF token, then billing will be handled directly on your HF account, as a way to centralize all expenses.

๐Ÿ’ธ Also, PRO users get 2$ inference credits per month!

Read more in the announcement ๐Ÿ‘‰ https://huggingface.co/blog/inference-providers
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davanstrienย 
posted an update 6 days ago
clemย 
posted an update 7 days ago
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6774
AI is not a zero-sum game. Open-source AI is the tide that lifts all boats!
davanstrienย 
posted an update 7 days ago
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1938
๐ŸŒ Big step for multilingual AI data!

The Hugging Face community has rated educational content in languages spoken by 1.6 billion people! New additions:
โ€ข Japanese
โ€ข Italian
โ€ข Old High German

Learn more and contribute: https://huggingface.co/blog/davanstrien/fineweb2-community

These ratings can help enhance training data for major world languages.
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lewtunย 
posted an update 9 days ago
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9705
We are reproducing the full DeepSeek R1 data and training pipeline so everybody can use their recipe. Instead of doing it in secret we can do it together in the open!

๐Ÿงช Step 1: replicate the R1-Distill models by distilling a high-quality reasoning corpus from DeepSeek-R1.

๐Ÿง  Step 2: replicate the pure RL pipeline that DeepSeek used to create R1-Zero. This will involve curating new, large-scale datasets for math, reasoning, and code.

๐Ÿ”ฅ Step 3: show we can go from base model -> SFT -> RL via multi-stage training.

Follow along: https://github.com/huggingface/open-r1
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merveย 
posted an update 9 days ago
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4635
Oof, what a week! ๐Ÿฅต So many things have happened, let's recap! merve/jan-24-releases-6793d610774073328eac67a9

Multimodal ๐Ÿ’ฌ
- We have released SmolVLM -- tiniest VLMs that come in 256M and 500M, with it's retrieval models ColSmol for multimodal RAG ๐Ÿ’—
- UI-TARS are new models by ByteDance to unlock agentic GUI control ๐Ÿคฏ in 2B, 7B and 72B
- Alibaba DAMO lab released VideoLlama3, new video LMs that come in 2B and 7B
- MiniMaxAI released Minimax-VL-01, where decoder is based on MiniMax-Text-01 456B MoE model with long context
- Dataset: Yale released a new benchmark called MMVU
- Dataset: CAIS released Humanity's Last Exam (HLE) a new challenging MM benchmark

LLMs ๐Ÿ“–
- DeepSeek-R1 & DeepSeek-R1-Zero: gigantic 660B reasoning models by DeepSeek, and six distilled dense models, on par with o1 with MIT license! ๐Ÿคฏ
- Qwen2.5-Math-PRM: new math models by Qwen in 7B and 72B
- NVIDIA released AceMath and AceInstruct, new family of models and their datasets (SFT and reward ones too!)

Audio ๐Ÿ—ฃ๏ธ
- Llasa is a new speech synthesis model based on Llama that comes in 1B,3B, and 8B
- TangoFlux is a new audio generation model trained from scratch and aligned with CRPO

Image/Video/3D Generation โฏ๏ธ
- Flex.1-alpha is a new 8B pre-trained diffusion model by ostris similar to Flux
- tencent released Hunyuan3D-2, new 3D asset generation from images
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clemย 
posted an update 9 days ago
dylanebertย 
posted an update 9 days ago
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575
โš™๏ธ Convert .ply to .splat

i've created a simple space to convert .ply gaussian splat files to .splat format

dylanebert/ply-to-splat
merveย 
posted an update 10 days ago
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2199
smolagents can see ๐Ÿ”ฅ
we just shipped vision support to smolagents ๐Ÿค— agentic computers FTW

you can now:
๐Ÿ’ป let the agent get images dynamically (e.g. agentic web browser)
๐Ÿ“‘ pass images at the init of the agent (e.g. chatting with documents, filling forms automatically etc)
with few LoC change! ๐Ÿคฏ
you can use transformers models locally (like Qwen2VL) OR plug-in your favorite multimodal inference provider (gpt-4o, antrophic & co) ๐Ÿค 

read our blog http://hf.co/blog/smolagents-can-see
m-ricย 
posted an update 10 days ago
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2696
Today we make the biggest release in smolagents so far: ๐˜„๐—ฒ ๐—ฒ๐—ป๐—ฎ๐—ฏ๐—น๐—ฒ ๐˜ƒ๐—ถ๐˜€๐—ถ๐—ผ๐—ป ๐—บ๐—ผ๐—ฑ๐—ฒ๐—น๐˜€, ๐˜„๐—ต๐—ถ๐—ฐ๐—ต ๐—ฎ๐—น๐—น๐—ผ๐˜„๐˜€ ๐˜๐—ผ ๐—ฏ๐˜‚๐—ถ๐—น๐—ฑ ๐—ฝ๐—ผ๐˜„๐—ฒ๐—ฟ๐—ณ๐˜‚๐—น ๐˜„๐—ฒ๐—ฏ ๐—ฏ๐—ฟ๐—ผ๐˜„๐˜€๐—ถ๐—ป๐—ด ๐—ฎ๐—ด๐—ฒ๐—ป๐˜๐˜€! ๐Ÿฅณ

Our agents can now casually open up a web browser, and navigate on it by scrolling, clicking elements on the webpage, going back, just like a user would.

The demo below shows Claude-3.5-Sonnet browsing GitHub for task: "Find how many commits the author of the current top trending repo did over last year."
Hi @mlabonne !

Go try it out, it's the most cracked agentic stuff I've seen in a while ๐Ÿคฏ (well, along with OpenAI's Operator who beat us by one day)

For more detail, read our announcement blog ๐Ÿ‘‰ https://huggingface.co/blog/smolagents-can-see
The code for the web browser example is here ๐Ÿ‘‰ https://github.com/huggingface/smolagents/blob/main/examples/vlm_web_browser.py
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anditoย 
posted an update 11 days ago
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1479
๐—œ๐—ป๐˜๐—ฟ๐—ผ๐—ฑ๐˜‚๐—ฐ๐—ถ๐—ป๐—ด ๐˜๐—ต๐—ฒ ๐˜„๐—ผ๐—ฟ๐—น๐—ฑ'๐˜€ ๐˜€๐—บ๐—ฎ๐—น๐—น๐—ฒ๐˜€๐˜ ๐˜ƒ๐—ถ๐˜€๐—ถ๐—ผ๐—ป ๐—น๐—ฎ๐—ป๐—ด๐˜‚๐—ฎ๐—ด๐—ฒ ๐—บ๐—ผ๐—ฑ๐—ฒ๐—น!

Weโ€™re thrilled to share ๐—ฆ๐—บ๐—ผ๐—น๐—ฉ๐—Ÿ๐—  (256M & 500M)โ€”the smallest Visual Language Models ever built. Think: running on <1GB of GPU memoryโ€”you can fine-tune it on your laptop and run it on your toaster!

Why Itโ€™s Game-Changing:
- ๐—ข๐˜‚๐˜๐—ฝ๐—ฒ๐—ฟ๐—ณ๐—ผ๐—ฟ๐—บ๐˜€ ๐—Ÿ๐—ฎ๐—ฟ๐—ด๐—ฒ๐—ฟ ๐— ๐—ผ๐—ฑ๐—ฒ๐—น๐˜€: Even the 256M model surpasses our SOTA 80B-parameter model from just 17 months ago. Over 300x reduction!
๐— ๐—ถ๐—ด๐—ต๐˜๐˜† ๐—˜๐—ณ๐—ณ๐—ถ๐—ฐ๐—ถ๐—ฒ๐—ป๐—ฐ๐˜†: The 256M version delivers 80% of our 2.2B modelโ€™s performance, and the 500M version hits 90%
๐—Ÿ๐—ถ๐—ด๐—ต๐˜๐—ป๐—ถ๐—ป๐—ด-๐—™๐—ฎ๐˜€๐˜ ๐—ฆ๐—ฒ๐—ฎ๐—ฟ๐—ฐ๐—ต: SmolVLM integrates with ColiPali for state-of-the-art retrieval speedsโ€”on par with models 10x bigger. That means cheaper, faster indexing and real-world impact.

Whatโ€™s New Under the Hood:
- ๐—ก๐—ฒ๐˜„ ๐—ฉ๐—ถ๐˜€๐—ถ๐—ผ๐—ป ๐—˜๐—ป๐—ฐ๐—ผ๐—ฑ๐—ฒ๐—ฟ: Smaller overall size (400M -> 93M), but with higher resolution.
- ๐—›๐—ถ๐—ด๐—ต๐—ฒ๐—ฟ ๐—ฃ๐—ถ๐˜…๐—ฒ๐—น๐˜€/๐—ง๐—ผ๐—ธ๐—ฒ๐—ป: 4096 vs. 1820โ€”more efficient image processing.
- ๐—ฆ๐—บ๐—ฎ๐—ฟ๐˜ ๐—ง๐—ผ๐—ธ๐—ฒ๐—ป๐—ถ๐˜‡๐—ฎ๐˜๐—ถ๐—ผ๐—ป: Faster training and a performance boost.

Check our blog: https://huggingface.co/blog/smolervlm
The models: HuggingFaceTB/smolvlm-256m-and-500m-6791fafc5bb0ab8acc960fb0
The demo: HuggingFaceTB/SmolVLM-256M-Demo
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merveย 
posted an update 17 days ago
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2545
Everything that happened this week in open AI, a recap ๐Ÿค  merve/jan-17-releases-678a673a9de4a4675f215bf5

๐Ÿ‘€ Multimodal
- MiniCPM-o 2.6 is a new sota any-to-any model by OpenBMB
(vision, speech and text!)
- VideoChat-Flash-Qwen2.5-2B is new video multimodal models by OpenGVLab that come in sizes 2B & 7B in resolutions 224 & 448
- ByteDance released larger SA2VA that comes in 26B parameters
- Dataset: VRC-Bench is a new diverse benchmark for multimodal LLM reasoning performance

๐Ÿ’ฌ LLMs
- MiniMax-Text-01 is a new huge language model (456B passive 45.9B active params) by MiniMaxAI with context length of 4M tokens ๐Ÿคฏ
- Dataset: Sky-T1-data-17k is a diverse dataset used to train Sky-T1-32B
- kyutai released Helium-1-Preview-2B is a new small multilingual LM
- Wayfarer-12B is a new LLM able to write D&D ๐Ÿง™๐Ÿปโ€โ™‚๏ธ
- ReaderLM-v2 is a new HTML parsing model by Jina AI

- Dria released, Dria-Agent-a-3B, new agentic coding model (Pythonic function calling) based on Qwen2.5 Coder
- Unsloth released Phi-4, faster and memory efficient Llama 3.3

๐Ÿ–ผ๏ธ Vision
- MatchAnything is a new foundation model for matching
- FitDit is a high-fidelity VTON model based on DiT architecture

๐Ÿ—ฃ๏ธ Audio
- OuteTTS-0.3-1B is a new multilingual text-to-speech model with voice cloning and emotion control capabilities

๐Ÿ“– Retrieval
- lightblue released a new reranker based on Qwen2.5 LB-reranker-0.5B-v1.0 that can handle 95+ languages
- cde-small-v2 is a new sota small retrieval model by
@jxm
merveย 
posted an update 18 days ago
m-ricย 
posted an update 18 days ago
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1214
๐— ๐—ถ๐—ป๐—ถ๐— ๐—ฎ๐˜…'๐˜€ ๐—ป๐—ฒ๐˜„ ๐— ๐—ผ๐—˜ ๐—Ÿ๐—Ÿ๐—  ๐—ฟ๐—ฒ๐—ฎ๐—ฐ๐—ต๐—ฒ๐˜€ ๐—–๐—น๐—ฎ๐˜‚๐—ฑ๐—ฒ-๐—ฆ๐—ผ๐—ป๐—ป๐—ฒ๐˜ ๐—น๐—ฒ๐˜ƒ๐—ฒ๐—น ๐˜„๐—ถ๐˜๐—ต ๐Ÿฐ๐—  ๐˜๐—ผ๐—ธ๐—ฒ๐—ป๐˜€ ๐—ฐ๐—ผ๐—ป๐˜๐—ฒ๐˜…๐˜ ๐—น๐—ฒ๐—ป๐—ด๐˜๐—ต ๐Ÿ’ฅ

This work from Chinese startup @MiniMax-AI introduces a novel architecture that achieves state-of-the-art performance while handling context windows up to 4 million tokens - roughly 20x longer than current models. The key was combining lightning attention, mixture of experts (MoE), and a careful hybrid approach.

๐—ž๐—ฒ๐˜† ๐—ถ๐—ป๐˜€๐—ถ๐—ด๐—ต๐˜๐˜€:

๐Ÿ—๏ธ MoE with novel hybrid attention:
โ€ฃ Mixture of Experts with 456B total parameters (45.9B activated per token)
โ€ฃ Combines Lightning attention (linear complexity) for most layers and traditional softmax attention every 8 layers

๐Ÿ† Outperforms leading models across benchmarks while offering vastly longer context:
โ€ฃ Competitive with GPT-4/Claude-3.5-Sonnet on most tasks
โ€ฃ Can efficiently handle 4M token contexts (vs 256K for most other LLMs)

๐Ÿ”ฌ Technical innovations enable efficient scaling:
โ€ฃ Novel expert parallel and tensor parallel strategies cut communication overhead in half
โ€ฃ Improved linear attention sequence parallelism, multi-level padding and other optimizations achieve 75% GPU utilization (that's really high, generally utilization is around 50%)

๐ŸŽฏ Thorough training strategy:
โ€ฃ Careful data curation and quality control by using a smaller preliminary version of their LLM as a judge!

Overall, not only is the model impressive, but the technical paper is also really interesting! ๐Ÿ“
It has lots of insights including a great comparison showing how a 2B MoE (24B total) far outperforms a 7B model for the same amount of FLOPs.

Read it in full here ๐Ÿ‘‰ MiniMax-01: Scaling Foundation Models with Lightning Attention (2501.08313)
Model here, allows commercial use <100M monthly users ๐Ÿ‘‰ MiniMaxAI/MiniMax-Text-01