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Model: Nanami14138/qwen3-4b-instruct-code-agent Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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language:
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- en
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- zh
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datasets:
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- m-a-p/Code-Feedback
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metrics:
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- pass@k
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base_model: Qwen/Qwen3-4B-Instruct
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tags:
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- code
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- agent
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- react
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- code-review
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- lora
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- unsloth
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- qwen3
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library_name: transformers
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pipeline_tag: text-generation
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model-index:
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- name: Qwen3-4B-CodeAgent
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results:
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- task:
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type: text-generation
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name: Code Generation
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dataset:
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name: HumanEval (10-problem subset)
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type: openai/openai_humaneval
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metrics:
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- name: Pass@1
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type: pass@1
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value: 62.6
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- name: Pass@3
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type: pass@3
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value: 75.61
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---
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# Qwen3-4B-CodeAgent
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A fine-tuned code execution and Code Review agent based on Qwen3-4B-Instruct, trained to follow a structured ReAct (Plan → Execute → Reflect → Finish) workflow with XML-formatted responses.
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## Model Description
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This model is a LoRA fine-tuned version of [Qwen3-4B-Instruct](https://huggingface.co/Qwen/Qwen3-4B) designed to function as an autonomous coding agent. It generates structured XML responses that can be parsed by an orchestration framework to execute code, review results, and iteratively debug.
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| Attribute | Value |
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|-----------|-------|
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| Base Model | Qwen3-4B-Instruct (3.6B params) |
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| Architecture | Qwen3ForCausalLM, 36 layers, 2560 hidden size, GQA (32 heads / 8 KV heads) |
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| Fine-tuning Method | LoRA (4-bit quantization + LoRA r=32, alpha=32) |
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| Framework | Unsloth + TRL SFTTrainer |
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| Training Data | m-a-p/Code-Feedback (~47K train samples) |
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| Context Length | 4096 tokens |
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| Precision | bfloat16 (merged weights) |
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## Intended Use
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This model is designed for building code agent systems that need structured, parseable output. It is suitable for:
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- Automated code generation with execution feedback loops
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- Code review and iterative debugging pipelines
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- Tool-augmented LLM applications with sandbox execution
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- Educational coding assistants
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## Output Format
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The model outputs XML-structured responses following a ReAct workflow:
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```xml
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<agent_response>
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<node>Plan</node>
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<next_node>Execute</next_node>
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<content>
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## Analysis
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The task requires implementing a binary search algorithm.
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## Plan
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1. Define the function signature
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2. Implement iterative binary search
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3. Handle edge cases (empty array, target not found)
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</content>
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</agent_response>
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```
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### Node Types
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| Node | Trigger | Content | Next Node |
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|------|---------|---------|-----------|
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| **Plan** | User sends a task | Markdown-formatted solution plan | Execute |
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| **Execute** | After Plan or Reflect | `{"tool_name": "python_sandbox", "arguments": {"code": "..."}}` | Execute |
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| **Reflect** | Execute fails (exit_code=1) | Root cause analysis and fix direction | Execute |
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| **Finish** | Execute succeeds (exit_code=0) | Task summary | Finish |
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### Standard Workflow
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```
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Plan → Execute → (failure → Reflect → Execute → ...) → Finish
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```
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## Usage
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### With Transformers
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "Nanami14138/qwen3-4b-instruct-code-agent"
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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## 🛠️ Prompting Strategy (系统提示词策略)
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本模型被设计为一个基于 ReAct 框架的智能 Code Agent。为了让模型严格按照状态机（Plan -> Execute -> Reflect -> Finish）运行，并输出结构化的 XML 格式，**强烈建议在推理时使用以下 System Prompt**：
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system_prompt = """你是一个专业的代码执行与Code Review智能Agent，遵循ReAct工作流。
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## 输出格式
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你的每一次回复都必须严格使用以下XML格式：
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<agent_response>
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<node>当前节点</node>
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<next_node>下一个节点</next_node>
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<content>输出内容</content>
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</agent_response>
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## 节点定义
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### Plan（规划）
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- 触发：收到用户任务后立即进入
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- <content>：分析任务需求，以 Markdown 格式输出解决方案规划
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- <next_node>：Execute
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### Execute（执行）
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- 触发：Plan 或 Reflect 之后进入
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- <content>：输出 {"tool_name": "python_sandbox", "arguments": {"code": "你的代码"}}
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- <next_node>：Execute（等待执行结果）
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### Reflect（反思）
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- 触发：Execute 执行失败（exit_code=1）后进入
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- <content>：分析失败原因，定位根因，给出修正方向
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- <next_node>：Execute（修正后重新执行）
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### Finish（完成）
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- 触发：Execute 执行成功（exit_code=0）后进入
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- <content>：输出任务总结
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- <next_node>：Finish
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## 标准工作流
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Plan → Execute → (失败 → Reflect → Execute → ...) → Finish"""
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": "任务：Write a Python function to check if a number is prime.\n\n当前状态：Start"}
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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with torch.no_grad():
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output = model.generate(**inputs, max_new_tokens=1024, temperature=0.1, top_p=0.95)
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response = tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
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print(response)
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```
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### With Unsloth (Faster Inference)
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```python
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from unsloth import FastLanguageModel
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name="your-username/qwen3-4b-code-agent",
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max_seq_length=4096,
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load_in_4bit=True,
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)
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FastLanguageModel.for_inference(model)
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# Then use the same message format as above
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```
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## Training Details
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### Data
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Trained on [m-a-p/Code-Feedback](https://huggingface.co/datasets/m-a-p/Code-Feedback), a multi-turn code conversation dataset with ~66K examples. The data was processed into three pools:
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| Pool | Description | Train Samples | Ratio |
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|------|-------------|---------------|-------|
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| Pool A (Base SFT) | Single-turn code Q&A, plain text | 117 | 0.2% |
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| Pool B (Code Review) | Multi-turn debug/review → ReAct XML format | 29,562 | 62.3% |
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| Pool C (Discussion) | Multi-turn code discussion → ReAct XML format | 17,737 | 37.4% |
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The system prompt is injected at training time (not stored in the data) to ensure consistent behavior.
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### Hyperparameters
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| Parameter | Value |
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|-----------|-------|
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| LoRA rank (r) | 32 |
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| LoRA alpha | 32 |
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| LoRA target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
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| Learning rate | 2e-4 (cosine schedule) |
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| Warmup ratio | 0.1 |
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| Batch size | 4 × 4 (gradient accumulation) = 16 effective |
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| Max sequence length | 4096 |
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| Precision | LoRA 4-bit (training), bfloat16 (merged) |
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| Optimizer | AdamW 8-bit |
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| Epochs | 3 (stopped early at ~5.8% progress, step 620/8892) |
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### Training Curve
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| Step | Train Loss | Eval Loss |
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|------|-----------|-----------|
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| 20 | 1.927 | 1.905 |
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| 100 | 0.649 | 0.573 |
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| 200 | 0.463 | 0.454 |
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| 300 | 0.412 | 0.422 |
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| 400 | 0.413 | 0.409 |
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| 500 | 0.374 | 0.401 |
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| 600 | 0.383 | 0.397 |
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Loss decreased from 1.90 to 0.40 with no signs of overfitting. The checkpoint at step 620 was merged for this release.
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### Hardware
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- 8× NVIDIA L20 (48GB each), single-GPU training via LoRA
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## Evaluation
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### HumanEval (10-problem subset)
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| Metric | Score |
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|--------|-------|
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| Pass@1 | 62.6% |
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| Pass@2 | 71.14% |
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| Pass@3 | 75.61% |
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| Avg tokens/problem | 215.2 |
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Evaluation was conducted on a 10-problem subset of HumanEval. Full 164-problem evaluation is planned.
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## Limitations
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- **Early checkpoint**: This model was merged at step 620 out of 8892 total steps (~3.4% of training). Performance will likely improve with continued training.
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- **English-centric data**: The training data (Code-Feedback) is predominantly in English. Chinese language coding tasks may have lower quality.
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- **XML format dependency**: The model is trained to output structured XML. Without the system prompt, it may not follow the expected format.
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- **No real execution**: The training data simulates tool responses; the model has not been trained with actual code execution feedback.
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- **Limited code languages**: While the training data covers multiple languages, Python is heavily overrepresented.
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- **Hallucination risk**: Like all LLMs, the model may generate plausible but incorrect code, especially for complex algorithms or domain-specific tasks.
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## Ethical Considerations
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- The model should not be used to generate malicious code or exploit vulnerabilities.
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- Generated code should always be reviewed by a human before deployment in production systems.
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- The model may reproduce biases present in the training data (e.g., coding style preferences, library choices).
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## Citation
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If you use this model, please cite the base model and training dataset:
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```bibtex
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@article{qwen3,
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title={Qwen3 Technical Report},
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author={Qwen Team},
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year={2025}
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}
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@misc{code-feedback,
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title={Code-Feedback: Multi-turn Code Conversation Dataset},
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author={m-a-p},
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url={https://huggingface.co/datasets/m-a-p/Code-Feedback}
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}
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```
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