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nicolo

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reacted to lewtun's post with 👍 3 days ago
We outperform Llama 70B with Llama 3B on hard math by scaling test-time compute 🔥 How? By combining step-wise reward models with tree search algorithms :) We show that smol models can match or exceed the performance of their much larger siblings when given enough "time to think" We're open sourcing the full recipe and sharing a detailed blog post. In our blog post we cover: 📈 Compute-optimal scaling: How we implemented DeepMind's recipe to boost the mathematical capabilities of open models at test-time. 🎄 Diverse Verifier Tree Search (DVTS): An unpublished extension we developed to the verifier-guided tree search technique. This simple yet effective method improves diversity and delivers better performance, particularly at large test-time compute budgets. 🧭 Search and Learn: A lightweight toolkit for implementing search strategies with LLMs and built for speed with vLLM Here's the links: - Blog post: https://huggingface.co/spaces/HuggingFaceH4/blogpost-scaling-test-time-compute - Code: https://github.com/huggingface/search-and-learn Enjoy!
reacted to burtenshaw's post with ❤️ 10 days ago
Quick update from week 1 of smol course. The community is taking the driving seat and using the material for their own projects. If you want to do the same, join in! - we have ongoing translation projects in Korean, Vietnamese, Portuguese, and Spanish - 3 chapters are ready for students. On topics like, instruction tuning, preference alignment, and parameter efficient fine tuning - 3 chapters are in progress on evaluation, vision language models, and synthetic data. - around 780 people have forked the repo to use it for learning, teaching, sharing. ⏭️ Next step is to support people that want to use the course for teaching, content creation, internal knowledge sharing, or anything. If you're into this. Drop an issue or PR REPO: https://buff.ly/3ZCMKX2 discord channel: https://buff.ly/4f9F8jA
liked a dataset 10 days ago
Xkev/LLaVA-CoT-100k
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