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PleIAs/Pleias-1.2b-Preview
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We outperform Llama 70B with Llama 3B on hard math by scaling test-time compute š„ 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: š Compute-optimal scaling: How we implemented DeepMind's recipe to boost the mathematical capabilities of open models at test-time. š Diverse Verifier Tree Search (DVTS): An unpublished extension we developed to the verifier-guided tree search technique.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. š§ 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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Doing some testing with HunyuanVideo on the Hugging Face Inference Endpoints š¤ prompt: "a Shiba Inu is acting as a DJ, he wears sunglasses and is mixing and scratching with vinyl discs at a Ibiza sunny sand beach party" 1280x720, 22 steps, 121 frames There are still some things to iron out regarding speed and memory usage, right now it takes 20min on an A100 (see attached charts) but you can check it out here: https://huggingface.co/jbilcke-hf/HunyuanVideo-for-InferenceEndpoints There are various things I want to try like the 100% diffusers version and other models (LTX-Video..)
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FineWeb: decanting the web for the finest text data at scale
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