Nanami14138/qwen3-4b-instruct-code-agent
Nanami14138/qwen3-4b-instruct-code-agent is a 4 billion parameter Qwen3-based instruction-tuned model, specifically fine-tuned as a code execution and code review agent. It is designed to follow a structured ReAct workflow, generating XML-formatted responses for automated code generation, iterative debugging, and tool-augmented LLM applications. The model excels at producing parseable output for orchestration frameworks, enabling dynamic interaction with code execution environments.
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Model Overview
Nanami14138/qwen3-4b-instruct-code-agent is a LoRA fine-tuned version of the Qwen3-4B-Instruct model, optimized to function as an autonomous coding agent. It processes tasks and generates structured XML responses, adhering to a ReAct (Plan → Execute → Reflect → Finish) workflow. This design allows for seamless integration with orchestration frameworks that can parse its output to execute code, review results, and facilitate iterative debugging.
Key Capabilities
- Structured Output: Generates XML-formatted responses for each step of the ReAct workflow, ensuring parseable and actionable output.
- Code Agent Workflow: Implements a robust
Plan->Execute->Reflect->Finishstate machine for systematic problem-solving. - Iterative Debugging: Features a
Reflectnode to analyze execution failures, identify root causes, and guide corrective actions. - Tool Integration: Designed to interact with external tools like
python_sandboxfor code execution. - Code Generation & Review: Fine-tuned on the
m-a-p/Code-Feedbackdataset, specializing in multi-turn code conversations, generation, and review.
Ideal Use Cases
This model is particularly well-suited for:
- Building automated code generation systems with integrated execution feedback loops.
- Developing code review and iterative debugging pipelines.
- Creating tool-augmented LLM applications that require sandbox execution.
- Powering educational coding assistants that guide users through problem-solving.