nuriyev/chess-llm
TEXT GENERATIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kPublished:Jan 9, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
nuriyev/chess-llm is a 4 billion parameter language model, finetuned from unsloth/Qwen3-4B-Instruct-2507, with a context length of 40960 tokens. Developed by nuriyev, this model was optimized for training speed using Unsloth and Huggingface's TRL library. Its primary use case is specialized applications requiring a Qwen3-based model with efficient finetuning.
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nuriyev/chess-llm Overview
nuriyev/chess-llm is a 4 billion parameter language model, finetuned by nuriyev from the unsloth/Qwen3-4B-Instruct-2507 base model. This model leverages a substantial 40960 token context length, making it suitable for tasks requiring extensive input processing.
Key Capabilities
- Efficient Finetuning: This model was trained significantly faster using the Unsloth library in conjunction with Huggingface's TRL library, indicating an optimized training process.
- Qwen3 Architecture: Built upon the Qwen3 architecture, it inherits the foundational capabilities of that model family.
- Extended Context Window: Features a 40960 token context length, allowing for processing and understanding of longer sequences of text.
Good for
- Applications requiring Qwen3-based models: Ideal for developers seeking a specialized Qwen3 variant.
- Projects benefiting from efficient training: Suitable for use cases where rapid iteration and finetuning are advantageous.
- Tasks with long input sequences: The extended context window supports applications that need to process large amounts of text.