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ребята из HF 🤗 написали нам
https://huggingface.co/spaces/HuggingFaceH4/blogpost-scaling-test-time-compute

Про подходы к test time compute (какой поиск можно делать, использовать верификаторы, самовалидацию и тд), как это масштабировать, как сделать на опенсурсовских моделях (на примере LLama 3.2)

вполне стоит почитать

За основу взяли статью от ДипМаинда

Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
https://arxiv.org/abs/2408.03314

код нашел тут:
https://github.com/huggingface/search-and-learn

сейчас реализовали на llama 3.2 1B подходы с Best of N, Beam Search, Diverse Verifier Beam Search (DVTS)
инструкция по запускам https://github.com/huggingface/search-and-learn/tree/main/recipes



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ребята из HF 🤗 написали нам
https://huggingface.co/spaces/HuggingFaceH4/blogpost-scaling-test-time-compute

Про подходы к test time compute (какой поиск можно делать, использовать верификаторы, самовалидацию и тд), как это масштабировать, как сделать на опенсурсовских моделях (на примере LLama 3.2)

вполне стоит почитать

За основу взяли статью от ДипМаинда

Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
https://arxiv.org/abs/2408.03314

код нашел тут:
https://github.com/huggingface/search-and-learn

сейчас реализовали на llama 3.2 1B подходы с Best of N, Beam Search, Diverse Verifier Beam Search (DVTS)
инструкция по запускам https://github.com/huggingface/search-and-learn/tree/main/recipes

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