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Model: Nanami14138/qwen3-4b-instruct-code-agent
Source: Original Platform

.gitattributes

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+qwen3-4b-instruct-code-agent-q4_k_m.gguf filter=lfs diff=lfs merge=lfs -text

README.md

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

added_tokens.json

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+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

config.json

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+{
+    "architectures": [
+        "Qwen3ForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 151643,
+    "eos_token_id": 151645,
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 2560,
+    "initializer_range": 0.02,
+    "intermediate_size": 9728,
+    "layer_types": [
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention"
+    ],
+    "max_position_embeddings": 40960,
+    "max_window_layers": 36,
+    "model_type": "qwen3",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 36,
+    "num_key_value_heads": 8,
+    "pad_token_id": 151643,
+    "rms_norm_eps": 1e-06,
+    "rope_scaling": null,
+    "rope_theta": 1000000,
+    "sliding_window": null,
+    "tie_word_embeddings": true,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.55.0",
+    "unsloth_version": "2025.8.4",
+    "use_cache": true,
+    "use_sliding_window": false,
+    "vocab_size": 151936
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "max_length": 40960,
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.55.0"
+}

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+    "<|box_end|>",
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+}