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Austin9
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8 days ago
We outperform Llama 70B with Llama 3B on hard math by scaling test-time compute π₯ 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: π Compute-optimal scaling: How we implemented DeepMind's recipe to boost the mathematical capabilities of open models at test-time. π Diverse Verifier Tree Search (DVTS): An unpublished extension we developed to the verifier-guided tree search technique.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. π§ 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!
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28 days ago
beomi/KoAlpaca-RealQA-Solar-Ko-Recovery-11B-LoRA-ChatML:
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beomi/KoAlpaca-RealQA-Solar-Ko-Recovery-11B-Merged:
merged μ λͺ¨λΈκ³Όμ μ°¨μ΄λ λκ°μ?
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