Model Overview

The codingmonster1234/chess-sft-modelv2 is a 4 billion parameter instruction-tuned language model, fine-tuned from the Qwen/Qwen3-4B-Instruct-2507 base model. It was developed by codingmonster1234 and trained using Supervised Fine-Tuning (SFT) with the TRL library.

Key Capabilities

• Instruction Following: Designed to respond to user prompts and instructions effectively, building on the Qwen3-4B-Instruct architecture.
• Text Generation: Capable of generating coherent and contextually relevant text based on given inputs.
• Context Handling: Benefits from a substantial 32768 token context window, allowing for processing longer inputs and generating more extended responses.

Training Details

The model's training procedure involved SFT, utilizing specific versions of popular machine learning frameworks:

• TRL: 0.29.1
• Transformers: 5.4.0
• Pytorch: 2.11.0
• Datasets: 4.8.4
• Tokenizers: 0.22.2

This fine-tuned model is suitable for various natural language processing tasks where instruction-following and robust text generation are required.