The-Swarm-Corporation/DPO-MCTS-ToT-Training
This module implements a post-training mechanism that allows a language model to explore various reasoning branches (chain-of-thoughts) using a Monte Carlo Tree Search (MCTS) framework. It selects the branch with the best answer using a cosine similarity evaluator that compares the candidate answer to a known correct answer.
This is for AI developers looking to improve the reasoning capabilities of their language models. It takes a pre-trained language model and a known correct answer as input. It then explores various reasoning paths, evaluates each, and outputs the best answer, enhancing the model's accuracy and decision-making.
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Use this if you are a machine learning engineer or researcher developing advanced language models and need to improve their accuracy and reasoning by exploring multiple potential solutions.
Not ideal if you are looking for a plug-and-play solution for a business user or if your application does not require complex, multi-step reasoning from a language model.
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Python
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MIT
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Last pushed
Feb 11, 2025
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