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lvwerra  authored a paper 2 days ago
Towards Best Practices for Open Datasets for LLM Training
guipenedo  authored a paper 2 days ago
Towards Best Practices for Open Datasets for LLM Training
thomwolf  authored a paper 2 days ago
Towards Best Practices for Open Datasets for LLM Training
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ariG23498 
posted an update 2 days ago
m-ric 
posted an update 2 days ago
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761
𝗠𝗶𝗻𝗶𝗠𝗮𝘅'𝘀 𝗻𝗲𝘄 𝗠𝗼𝗘 𝗟𝗟𝗠 𝗿𝗲𝗮𝗰𝗵𝗲𝘀 𝗖𝗹𝗮𝘂𝗱𝗲-𝗦𝗼𝗻𝗻𝗲𝘁 𝗹𝗲𝘃𝗲𝗹 𝘄𝗶𝘁𝗵 𝟰𝗠 𝘁𝗼𝗸𝗲𝗻𝘀 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗹𝗲𝗻𝗴𝘁𝗵 💥

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.

𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀:

🏗️ MoE with novel hybrid attention:
‣ Mixture of Experts with 456B total parameters (45.9B activated per token)
‣ Combines Lightning attention (linear complexity) for most layers and traditional softmax attention every 8 layers

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

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

🎯 Thorough training strategy:
‣ 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! 📝
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 👉 MiniMax-01: Scaling Foundation Models with Lightning Attention (2501.08313)
Model here, allows commercial use <100M monthly users 👉 MiniMaxAI/MiniMax-Text-01
lvwerra 
authored a paper 2 days ago
guipenedo 
authored a paper 2 days ago
thomwolf 
authored a paper 2 days ago
m-ric 
posted an update 3 days ago
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2145
𝗪𝗲'𝘃𝗲 𝗷𝘂𝘀𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝘀𝗺𝗼𝗹𝗮𝗴𝗲𝗻𝘁𝘀 𝘃𝟭.𝟯.𝟬 🚀, and it comes with a major feature: you can now log agent runs using OpenTelemetry to inspect them afterwards! 📊

This interactive format is IMO much easier to inspect big multi-step runs than endless console logs.

The setup is very easy, in a few lines of code.

Find a tutorial here 👉 https://huggingface.co/docs/smolagents/tutorials/inspect_runs
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fdaudens 
posted an update 3 days ago
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1653
AI agents are coming. But who's in control?

@meg , one of the best researchers in AI ethics, makes a critical point about autonomy: fully autonomous systems carry unknowable risks because they operate on computer logic rather than human logic.

The solution? Build systems that support & assist rather than override human decisions.

I highly recommend reading the blog post written by Meg, @evijit @sasha and @giadap . They define different levels of agent autonomy & provide a values-based analysis of risks, benefits, and uses of AI agents to help you make better decisions.

👉 https://huggingface.co/blog/ethics-soc-7
fdaudens 
posted an update 4 days ago
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2235
🔥 The AI Agent hype is real! This blog post deep dives into everything you need to know before deploying them: from key definitions to practical recommendations. A must-read for anyone building the future of autonomous systems.

📊 Key insight: A clear table breaking down the 5 levels of AI agents - from simple processors to fully autonomous systems. Essential framework for understanding where your agent stands on the autonomy spectrum

⚖️ Deep analysis of 15 core values reveals critical trade-offs: accuracy, privacy, safety, equity & more. The same features that make agents powerful can make them risky. Understanding these trade-offs is crucial for responsible deployment

🎯 6 key recommendations for the road ahead:
- Create rigorous evaluation protocols
- Study societal effects
- Understand ripple effects
- Improve transparency
- Open source can make a positive difference
- Monitor base model evolution

Read the blog post: https://huggingface.co/blog/ethics-soc-7 Brillant work by @meg @evijit @sasha @giadap
meg 
posted an update 5 days ago
m-ric 
posted an update 6 days ago
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559
𝗢𝗦-𝗚𝗲𝗻𝗲𝘀𝗶𝘀: 𝗻𝗲𝘄 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗽𝗮𝗽𝗲𝗿 𝗽𝗿𝗼𝗽𝗼𝘀𝗲𝘀 𝗮 𝗻𝗼𝘃𝗲𝗹 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗱𝗮𝘁𝗮 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗺𝗲𝘁𝗵𝗼𝗱 𝗳𝗼𝗿 𝗖𝗹𝗮𝘂𝗱𝗲-𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿-𝗨𝘀𝗲-𝗹𝗶𝗸𝗲 𝗮𝗴𝗲𝗻𝘁𝘀, 𝘄𝗶𝘁𝗵 𝗶𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝘃𝗲 𝗿𝗲𝘀𝘂𝗹𝘁𝘀! 🔥

The main bottleneck in building GUI agents it to find training data.
GUI Agent trajectories are not easy to get by. Crowdsourcing trajectories, then manually annotating them, could be an option, but at scale, it's hard to do

You could use synthetic data generation (ask 1000s small existing GUI agents to solve tasks, keep only successful runs). But then it's hard to come up with many high level-tasks.

➡️ Well, a novel technique was just published that creates a new promising paradigm for synthetic data generation: Shanghai AI Lab researchers propose OS-Genesis, a novel way to create training data for GUI agents that flips the traditional approach on its head. Instead of starting with predefined tasks and having humans or machines execute them, OS-Genesis first explores the interface naturally, then derives meaningful tasks from those interactions.

🔍 Exploration-driven vs task-driven approach:
‣ Instead of starting with tasks, OS-Genesis first explores GUIs by clicking and interacting
‣ It then reverse-engineers high-level tasks from successful interaction patterns
‣ This leads to more natural and diverse training data than predefined tasks

🎯 Novel reward model for trajectory quality:
‣ Rather than discarding incomplete trajectories, OS-Genesis scores them based on coherence and completion
‣ This preserves valuable partial successes that would otherwise be wasted

🏆 Superior results across environments:
‣ Nearly doubles performance on AndroidWorld (9.8% → 17.4%)

By the way, this field of GUI agents is still in infancy, so you can still make a difference with "low-cost" setups: their paper gets SOTA results with only 8xA100!

Read the paper here 👉 OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis (2412.19723)
BrigitteTousi 
posted an update 8 days ago
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Community fine-tuned models are more carbon efficient than the models they are derived from! 🥳🌿

@alozowski @clefourrier @SaylorTwift @albertvillanova evaluated CO₂ emissions associated with model inference for over 3000 models on the Open LLM Leaderboard. Interesting trends and new insights emerged...👀

Blog Post: https://huggingface.co/blog/leaderboard-emissions-analysis

Leaderboard: open-llm-leaderboard/open_llm_leaderboard
m-ric 
posted an update 11 days ago
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4983
Since I published it on GitHub a few days ago,
Hugging Face's new agentic library 𝘀𝗺𝗼𝗹𝗮𝗴𝗲𝗻𝘁𝘀 has gathered nearly 4k stars 🤯

➡️ But we are just getting started on agents: so we are hiring an ML Engineer to join me and double down on this effort!

The plan is to build GUI agents: agents that can act on your computer with mouse & keyboard, like Claude Computer Use.

We will make it work better, and fully open. ✨

Sounds like something you'd like to do? Apply here 👉 https://apply.workable.com/huggingface/j/AF1D4E3FEB/
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thomwolf 
updated a Space 12 days ago
lewtun 
posted an update 12 days ago
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3267
I was initially pretty sceptical about Meta's Coconut paper [1] because the largest perf gains were reported on toy linguistic problems. However, these results on machine translation are pretty impressive!

https://x.com/casper_hansen_/status/1875872309996855343

Together with the recent PRIME method [2] for scaling RL, reasoning for open models is looking pretty exciting for 2025!

[1] Training Large Language Models to Reason in a Continuous Latent Space (2412.06769)
[2] https://huggingface.co/blog/ganqu/prime
clefourrier 
updated a Space 15 days ago
lewtun 
posted an update 19 days ago
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2130
This paper ( HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs (2412.18925)) has a really interesting recipe for inducing o1-like behaviour in Llama models:

* Iteratively sample CoTs from the model, using a mix of different search strategies. This gives you something like Stream of Search via prompting.
* Verify correctness of each CoT using GPT-4o (needed because exact match doesn't work well in medicine where there are lots of aliases)
* Use GPT-4o to reformat the concatenated CoTs into a single stream that includes smooth transitions like "hmm, wait" etc that one sees in o1
* Use the resulting data for SFT & RL
* Use sparse rewards from GPT-4o to guide RL training. They find RL gives an average ~3 point boost across medical benchmarks and SFT on this data already gives a strong improvement.

Applying this strategy to other domains could be quite promising, provided the training data can be formulated with verifiable problems!
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eliebak 
updated a Space 19 days ago
fdaudens 
posted an update 29 days ago
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🔍 From instruction-following to creative storytelling, dive into 2024's most impactful AI datasets! These gems are shaping everything from scientific research to video understanding.

Check it out: huggingface/open-source-ai-year-in-review-2024
m-ric 
posted an update 30 days ago
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After 6 years, BERT, the workhorse of encoder models, finally gets a replacement: 𝗪𝗲𝗹𝗰𝗼𝗺𝗲 𝗠𝗼𝗱𝗲𝗿𝗻𝗕𝗘𝗥𝗧! 🤗

We talk a lot about ✨Generative AI✨, meaning "Decoder version of the Transformers architecture", but this is only one of the ways to build LLMs: encoder models, that turn a sentence in a vector, are maybe even more widely used in industry than generative models.

The workhorse for this category has been BERT since its release in 2018 (that's prehistory for LLMs).

It's not a fancy 100B parameters supermodel (just a few hundred millions), but it's an excellent workhorse, kind of a Honda Civic for LLMs.

Many applications use BERT-family models - the top models in this category cumulate millions of downloads on the Hub.

➡️ Now a collaboration between Answer.AI and LightOn just introduced BERT's replacement: ModernBERT.

𝗧𝗟;𝗗𝗥:
🏛️ Architecture changes:
⇒ First, standard modernizations:
- Rotary positional embeddings (RoPE)
- Replace GeLU with GeGLU,
- Use Flash Attention 2
✨ The team also introduced innovative techniques like alternating attention instead of full attention, and sequence packing to get rid of padding overhead.

🥇 As a result, the model tops the game of encoder models:
It beats previous standard DeBERTaV3 for 1/5th the memory footprint, and runs 4x faster!

Read the blog post 👉 https://huggingface.co/blog/modernbert
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anton-l 
posted an update 30 days ago
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Introducing 📐𝐅𝐢𝐧𝐞𝐌𝐚𝐭𝐡: the best public math pre-training dataset with 50B+ tokens!
HuggingFaceTB/finemath

Math remains challenging for LLMs and by training on FineMath we see considerable gains over other math datasets, especially on GSM8K and MATH.

We build the dataset by:
🛠️ carefully extracting math data from Common Crawl;
🔎 iteratively filtering and recalling high quality math pages using a classifier trained on synthetic annotations to identify math reasoning and deduction.

We conducted a series of ablations comparing the performance of Llama-3.2-3B-Base after continued pre-training on FineMath and observe notable gains compared to the baseline model and other public math datasets.

We hope this helps advance the performance of LLMs on math and reasoning! 🚀
We’re also releasing all the ablation models as well as the evaluation code.

HuggingFaceTB/finemath-6763fb8f71b6439b653482c2