codingmonster1234/Llama-3.1-8B-Instruct-Chess-Reasoning-SFT-v2
The codingmonster1234/Llama-3.1-8B-Instruct-Chess-Reasoning-SFT-v2 is an 8 billion parameter instruction-tuned causal language model, fine-tuned from Meta's Llama-3.1-8B-Instruct. This model, developed by codingmonster1234, specializes in chess reasoning, leveraging a context length of 32768 tokens. It is specifically optimized for tasks requiring logical deduction and strategic understanding within the domain of chess.
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Model Overview
The codingmonster1234/Llama-3.1-8B-Instruct-Chess-Reasoning-SFT-v2 is an 8 billion parameter instruction-tuned model, fine-tuned from the base meta-llama/Llama-3.1-8B-Instruct architecture. This model has been specifically trained using Supervised Fine-Tuning (SFT) with the TRL framework, focusing on enhancing its reasoning capabilities within the domain of chess.
Key Capabilities
- Chess Reasoning: Optimized for understanding and generating responses related to chess strategies, moves, and logical deductions.
- Instruction Following: Benefits from the strong instruction-following base of Llama-3.1-8B-Instruct, further refined for domain-specific queries.
- Extended Context: Supports a context length of 32768 tokens, allowing for more complex and detailed chess-related prompts.
Training Details
The model was fine-tuned using the TRL library, a framework for Transformer Reinforcement Learning. The training process is logged and can be visualized via Weights & Biases, indicating a focus on specific chess reasoning datasets (though the specific dataset is not detailed in the provided README).
Good For
- Applications requiring AI to analyze chess positions or suggest moves.
- Educational tools for chess players.
- Research into domain-specific reasoning with large language models.