SALEETAI/coding-agent-qwen-sft-v2
SALEETAI/coding-agent-qwen-sft-v2 is a 7.6 billion parameter Qwen2-based model, fine-tuned by SALEETAI for coding agent tasks. This model leverages Unsloth and Huggingface's TRL library for efficient training, building upon the unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit base. With a 32768 token context length, it is optimized for code-related applications and complex programming instructions.
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Model Overview
SALEETAI/coding-agent-qwen-sft-v2 is a 7.6 billion parameter language model developed by SALEETAI, specifically fine-tuned for coding agent functionalities. It is based on the Qwen2 architecture and utilizes the unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit as its foundational model.
Key Characteristics
- Efficient Training: This model was trained using Unsloth and Huggingface's TRL library, enabling faster fine-tuning processes.
- Base Model: It builds upon the
Qwen2.5-Coder-7B-Instructseries, suggesting an inherent capability for instruction-following and code-centric tasks. - Context Length: Features a substantial context window of 32768 tokens, beneficial for handling larger codebases or complex multi-turn coding interactions.
Intended Use Cases
This model is designed for applications requiring a coding agent, such as:
- Automated code generation and completion.
- Assisting with programming tasks and debugging.
- Interpreting and responding to complex coding instructions.