codingmonster1234/chess-tool-use-grpo-336
The codingmonster1234/chess-tool-use-grpo-336 model is a 4 billion parameter instruction-tuned causal language model, fine-tuned from Qwen/Qwen3-4B-Instruct-2507 using TRL. This model is specifically trained for chess-related reasoning and tool-calling tasks, leveraging its 32768 token context length. It is optimized for scenarios requiring strategic understanding and interaction within the domain of chess.
Loading preview...
Model Overview
The codingmonster1234/chess-tool-use-grpo-336 is a 4 billion parameter instruction-tuned language model, fine-tuned from the base Qwen/Qwen3-4B-Instruct-2507 model. It was developed using the TRL (Transformers Reinforcement Learning) framework, indicating a focus on improving its performance through supervised fine-tuning (SFT).
Key Capabilities
- Specialized Fine-tuning: This model has undergone specific training to enhance its abilities in chess-related reasoning and tool-calling. This suggests it can process and respond to queries or commands pertaining to chess strategies, moves, or game states.
- Instruction Following: As an instruction-tuned model, it is designed to understand and execute user instructions effectively, particularly within its specialized domain.
- Context Length: With a context length of 32768 tokens, the model can process and retain a significant amount of information, which is beneficial for complex chess scenarios or extended dialogues.
Training Details
The model was trained using Supervised Fine-Tuning (SFT) with TRL. The training process was logged and can be visualized via Weights & Biases, providing transparency into its development. The framework versions used include TRL 1.12.0, Transformers 5.16.1, Pytorch 2.13.0, Datasets 5.0.1, and Tokenizers 0.23.1.
Good For
- Applications requiring AI assistance in chess analysis or strategy.
- Developing tools that interact with chess engines or game states.
- Research into specialized language model applications for specific game domains.