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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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28
added_tokens.json
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added_tokens.json
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{
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"</think>": 151668,
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"</tool_call>": 151658,
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"</tool_response>": 151666,
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"<think>": 151667,
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"<tool_call>": 151657,
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"<tool_response>": 151665,
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
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"<|endoftext|>": 151643,
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"<|file_sep|>": 151664,
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"<|fim_middle|>": 151660,
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"<|fim_pad|>": 151662,
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"<|fim_prefix|>": 151659,
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"<|fim_suffix|>": 151661,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644,
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"<|image_pad|>": 151655,
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"<|object_ref_end|>": 151647,
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"<|object_ref_start|>": 151646,
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"<|quad_end|>": 151651,
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"<|quad_start|>": 151650,
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"<|repo_name|>": 151663,
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"<|video_pad|>": 151656,
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"<|vision_end|>": 151653,
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"<|vision_pad|>": 151654,
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"<|vision_start|>": 151652
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}
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chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{{- messages[0].content + '\n\n' }}
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{%- endif %}
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{{- "# 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>" }}
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{%- for tool in tools %}
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{{- "\n" }}
|
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\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" }}
|
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{%- else %}
|
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for message in messages[::-1] %}
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- 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>')) %}
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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
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{%- endif %}
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{%- endfor %}
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||||
{%- for message in messages %}
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||||
{%- if message.content is string %}
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||||
{%- set content = message.content %}
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{%- else %}
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{%- set content = '' %}
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||||
{%- endif %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is string %}
|
||||
{%- set reasoning_content = message.reasoning_content %}
|
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{%- else %}
|
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{%- if '</think>' in content %}
|
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{%- 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 %}
|
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{%- if loop.last or (not loop.last and reasoning_content) %}
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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||||
{%- 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") %}
|
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{{- '<|im_end|>\n' }}
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{%- endif %}
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||||
{%- endif %}
|
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{%- endfor %}
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{%- 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 %}
|
||||
70
config.json
Normal file
70
config.json
Normal file
@@ -0,0 +1,70 @@
|
||||
{
|
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"architectures": [
|
||||
"Qwen3ForCausalLM"
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|
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14
generation_config.json
Normal file
14
generation_config.json
Normal file
@@ -0,0 +1,14 @@
|
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{
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|
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151388
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151388
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Load Diff
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qwen3-4b-instruct-code-agent-q4_k_m.gguf
Normal file
3
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Normal file
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|
||||
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31
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Normal file
31
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Normal file
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||||
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Normal file
BIN
tokenizer.json
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Normal file
Binary file not shown.
244
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244
tokenizer_config.json
Normal file
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||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user