ertghiu256/qwen3-4b-code-reasoning-v2
The ertghiu256/qwen3-4b-code-reasoning-v2 is a 4 billion parameter Qwen3 model developed by ertghiu256, fine-tuned for code reasoning and instruction. It was trained on nvidia/OpenCodeReasoning and nvidia/OpenCodeInstruct datasets, with a context length up to 32,000 tokens. This model specializes in code-related tasks, leveraging its training on specific code reasoning and instruction datasets.
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Overview
The ertghiu256/qwen3-4b-code-reasoning-v2 is a 4 billion parameter Qwen3 model, developed by ertghiu256. It has been specifically fine-tuned for code reasoning and instruction tasks, utilizing the nvidia/OpenCodeReasoning and nvidia/OpenCodeInstruct datasets. The training data was filtered to ensure a context length of up to 32,000 tokens, making it suitable for handling moderately long code snippets and instructions.
Key Capabilities
- Code Reasoning: Optimized for understanding and processing logical structures within code.
- Code Instruction Following: Designed to accurately interpret and execute code-related instructions.
- Efficient Training: Fine-tuned using Unsloth and Huggingface's TRL library, resulting in 2x faster training.
- Anchored Knowledge: Enhanced with a subset of
ChavyvAkvar/qwen3-coder-480b-distill-mini-Convertedfor broader code understanding.
Recommended Usage
This model is ideal for applications requiring robust code understanding and generation based on instructions. Recommended inference parameters include a temperature of 0.6-0.7, a repeat penalty of 1.2, top_p of 0.95, and top_k of 20, with enable_thinking set to True for potentially improved reasoning.