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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

MODEL_CARD.md

@@ -0,0 +1,199 @@
+---
+language:
+- en
+- code
+license: apache-2.0
+tags:
+- code
+- java
+- bug-fixing
+- code-repair
+- qwen2.5
+- supervised-fine-tuning
+base_model: Qwen/Qwen2.5-Coder-7B-Instruct
+model_type: qwen2
+pipeline_tag: text-generation
+---
+
+# HaiJava-Surgeon-Qwen2.5-Coder-7B-SFT-v1
+
+## Model Description
+
+**HaiJava-Surgeon-Qwen2.5-Coder-7B-SFT-v1** is a specialized code repair model fine-tuned on Java bug-fixing tasks. It is based on Qwen2.5-Coder-7B-Instruct and has been supervised fine-tuned (SFT) with LoRA adapters merged into the base model for optimal performance.
+
+### Model Details
+
+- **Model Name**: HaiJava-Surgeon-Qwen2.5-Coder-7B-SFT-v1
+- **Version**: v1.0
+- **Base Model**: [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct)
+- **Model Type**: Causal Language Model (Decoder-only Transformer)
+- **Architecture**: Qwen2ForCausalLM
+- **Parameters**: 7.61B
+- **Precision**: FP16
+- **Context Length**: 32,768 tokens
+- **Fine-tuning Method**: LoRA (Low-Rank Adaptation) merged into base
+- **Training Steps**: 1,000
+- **Release Date**: 2026-01-02
+
+### Intended Use
+
+This model is designed for:
+- **Java bug detection and repair**
+- **Syntax error correction**
+- **Logic bug fixing**
+- **Code quality improvement**
+- **Automated code review assistance**
+
+### Performance
+
+Evaluated on a 50-sample diverse test set:
+
+| Metric | Base Model | HaiJava-Surgeon v1 | Improvement |
+|--------|-----------|-------------------|-------------|
+| **Overall Accuracy** | 18% | **28%** | **+55.6%** |
+| **Syntax Errors** | 60% | **90%** | **+50%** |
+| **Logic Bugs** | 30% | **40%** | **+33%** |
+
+**Statistical Significance**: p-value = 0.0238* (significant at α=0.05)
+
+### Strengths
+- ✅ **Excellent** at syntax error detection and repair (90% accuracy)
+- ✅ **Good** at logic bug fixing (40% accuracy)
+- ✅ Shows generalization to JavaScript (50% accuracy on OOD samples)
+
+### Limitations
+- ⚠️ Struggles with API misuse detection (0% accuracy)
+- ⚠️ Limited edge case handling (0% accuracy)
+- ⚠️ Needs improvement on null pointer exception fixes (0% accuracy)
+- ⚠️ Limited Python support (0% accuracy on OOD samples)
+
+## Usage
+
+### Quick Start
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load model and tokenizer
+model = AutoModelForCausalLM.from_pretrained(
+    "HaiJava-Surgeon-Qwen2.5-Coder-7B-SFT-v1",
+    torch_dtype=torch.float16,
+    device_map="auto",
+    trust_remote_code=True
+)
+
+tokenizer = AutoTokenizer.from_pretrained(
+    "HaiJava-Surgeon-Qwen2.5-Coder-7B-SFT-v1",
+    trust_remote_code=True
+)
+
+# Prepare input
+buggy_code = """
+public class Example {
+    public static void main(String[] args) {
+        int x = 10
+        System.out.println(x);
+    }
+}
+"""
+
+prompt = f"Fix the bug in the following Java code:\n\n{buggy_code}"
+messages = [{"role": "user", "content": prompt}]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+# Generate fix
+inputs = tokenizer([text], return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=512,
+    do_sample=False,
+    pad_token_id=tokenizer.eos_token_id
+)
+
+# Decode response
+response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
+print(response)
+```
+
+### Recommended Generation Parameters
+
+**For Maximum Accuracy:**
+```python
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=1024,
+    do_sample=False,      # Greedy decoding
+    num_beams=5,          # Beam search
+)
+```
+
+**For Speed:**
+```python
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=256,
+    do_sample=False,
+)
+```
+
+## Training Details
+
+### Training Data
+- **Domain**: Java bug-fixing
+- **Categories**: Syntax errors, logic bugs, API misuse, edge cases, null handling
+- **Training Steps**: 1,000
+
+### Training Configuration
+- **Method**: LoRA (Low-Rank Adaptation)
+- **LoRA Rank (r)**: 16
+- **LoRA Alpha**: 32
+- **Target Modules**: q_proj, v_proj
+- **Dropout**: 0.05
+- **Optimizer**: AdamW
+- **Learning Rate**: 5e-5
+
+### Hardware
+- **GPU**: NVIDIA GPU with CUDA support
+- **Training Time**: ~2-3 hours
+- **Framework**: LLaMA-Factory + PyTorch
+
+## Evaluation
+
+Evaluated on 50 diverse samples covering:
+- Syntax errors (10 samples)
+- Logic bugs (10 samples)
+- API misuse (10 samples)
+- Edge cases (10 samples)
+- Null handling (5 samples)
+- Out-of-distribution: JavaScript (2 samples)
+- Out-of-distribution: Python (3 samples)
+
+**Evaluation Metrics:**
+- Exact match accuracy
+- Normalized edit distance
+- Statistical significance testing (paired t-test)
+
+## License
+
+This model inherits the license from the base model: [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct)
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{haijava-surgeon-v1,
+  title={HaiJava-Surgeon-Qwen2.5-Coder-7B-SFT-v1: A Specialized Java Bug-Fixing Model},
+  author={Your Name/Organization},
+  year={2026},
+  url={https://huggingface.co/your-username/HaiJava-Surgeon-Qwen2.5-Coder-7B-SFT-v1}
+}
+```
+
+## Acknowledgments
+
+- **Base Model**: Qwen Team (Alibaba Cloud)
+- **Fine-tuning Framework**: [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)
+- **Evaluation**: Custom 50-sample test suite
+

README.md

@@ -1,3 +1,250 @@
----
-license: apache-2.0
----
+# HaiJava-Surgeon-Qwen2.5-Coder-7B-SFT-v1
+
+**Model Name**: HaiJava-Surgeon-Qwen2.5-Coder-7B-SFT-v1
+**Model Type**: Supervised Fine-Tuned (SFT) - Merged LoRA + Base Model
+**Base Model**: Qwen/Qwen2.5-Coder-7B-Instruct
+**Fine-tuning**: checkpoint-1000 (1000 training steps on Java bug-fixing)
+**Version**: v1.0
+**Release Date**: 2026-01-02
+**Status**: ✅ Ready for Production / Further Training
+
+---
+
+## 📊 Model Performance
+
+This model is the result of merging checkpoint-1000 (LoRA adapter) into the base Qwen2.5-Coder-7B-Instruct model.
+
+### MultiPL-E Java Benchmark Results
+
+| Model | Pass@1 | Passed | Total | Improvement |
+|-------|--------|--------|-------|-------------|
+| **Base Model (Qwen2.5-Coder-7B-Instruct)** | 67.72% | 107 | 158 | Baseline |
+| **This Model (Fine-Tuned)** | **82.28%** | **130** | **158** | **+14.56%** ✅ |
+
+**Key Achievements**:
+- ✅ **+23 problems solved** compared to base model
+- ✅ **27 problems** where SFT passes but base fails
+- ✅ **103 problems** where both models pass
+
+**Benchmark Details**:
+- **Dataset**: MultiPL-E Java (158 programming problems translated from HumanEval)
+- **Evaluation Date**: 2026-01-08
+- **Temperature**: 0.0 (deterministic)
+- **Max Tokens**: 1024
+
+### Internal Evaluation Results (50-sample test set)
+
+| Metric | Base Model | This Model (Merged) | Improvement |
+|--------|-----------|---------------------|-------------|
+| **Overall Accuracy** | 9/50 (18%) | 14/50 (28%) | **+55.6%** ✅ |
+| **Syntax Errors** | 6/10 (60%) | 9/10 (90%) | **+50%** ✅ |
+| **Logic Bugs** | 3/10 (30%) | 4/10 (40%) | **+33%** ✅ |
+| **API Misuse** | 0/10 (0%) | 0/10 (0%) | No change |
+| **Edge Cases** | 0/10 (0%) | 0/10 (0%) | No change |
+| **OOD JavaScript** | 0/2 (0%) | 1/2 (50%) | **+50%** ✅ |
+
+**Statistical Significance**: p-value = 0.0238* (significant at α=0.05)
+
+---
+
+## 🎯 Use Cases
+
+### 1. Further Training
+Use this merged model as the base for continued fine-tuning:
+
+```yaml
+# LLaMA-Factory training config
+model_name_or_path: ./HaiJava-Surgeon-Qwen2.5-Coder-7B-SFT-v1
+finetuning_type: lora  # Can apply new LoRA on top
+lora_target: q_proj,v_proj
+```
+
+**Benefits**:
+- Start from improved baseline (28% accuracy vs 18%)
+- No adapter overhead during training
+- Can apply new LoRA adapters for specialized tasks
+
+### 2. Direct Inference
+Use for production inference without adapter loading:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained(
+    "./HaiJava-Surgeon-Qwen2.5-Coder-7B-SFT-v1",
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("./HaiJava-Surgeon-Qwen2.5-Coder-7B-SFT-v1")
+
+# No adapter loading needed!
+```
+
+**Benefits**:
+- Faster loading (no adapter merge at runtime)
+- Simpler deployment (single model, no adapter files)
+- Same performance as base + adapter
+
+### 3. Production Deployment
+Deploy directly to production environments:
+
+```bash
+# Copy to deployment server
+scp -r HaiJava-Surgeon-Qwen2.5-Coder-7B-SFT-v1 user@server:/models/
+
+# Use in production
+python inference_server.py --model /models/HaiJava-Surgeon-Qwen2.5-Coder-7B-SFT-v1
+```
+
+---
+
+## 📁 Model Files
+
+| File | Size | Description |
+|------|------|-------------|
+| `model-00001-of-00004.safetensors` | ~3.5GB | Model weights (shard 1) |
+| `model-00002-of-00004.safetensors` | ~3.5GB | Model weights (shard 2) |
+| `model-00003-of-00004.safetensors` | ~3.5GB | Model weights (shard 3) |
+| `model-00004-of-00004.safetensors` | ~3.5GB | Model weights (shard 4) |
+| `config.json` | ~1KB | Model configuration |
+| `tokenizer.json` | ~7MB | Tokenizer vocabulary |
+| `generation_config.json` | ~1KB | Generation parameters |
+
+**Total Size**: ~14GB
+
+---
+
+## 🔧 Training Details
+
+### Original LoRA Training (checkpoint-1000)
+- **Training Steps**: 1000
+- **LoRA Rank (r)**: 16
+- **LoRA Alpha**: 32
+- **Target Modules**: q_proj, v_proj
+- **Dropout**: 0.05
+- **Training Data**: Java bug-fixing samples
+
+### Merge Process
+- **Method**: `merge_and_unload()` from PEFT library
+- **Precision**: float16
+- **Merge Date**: 2026-01-02
+- **Verification**: Passed (model loads successfully)
+
+---
+
+## 🚀 Quick Start
+
+### Load for Inference
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model
+model = AutoModelForCausalLM.from_pretrained(
+    "./HaiJava-Surgeon-Qwen2.5-Coder-7B-SFT-v1",
+    torch_dtype=torch.float16,
+    device_map="auto",
+    trust_remote_code=True
+)
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(
+    "./HaiJava-Surgeon-Qwen2.5-Coder-7B-SFT-v1",
+    trust_remote_code=True
+)
+
+# Generate
+prompt = "Fix the bug in this Java code: int x = 10"
+messages = [{"role": "user", "content": prompt}]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)
+response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
+print(response)
+```
+
+### Load for Further Training
+```python
+from transformers import AutoModelForCausalLM, TrainingArguments
+from peft import LoraConfig, get_peft_model
+
+# Load merged model as base
+base_model = AutoModelForCausalLM.from_pretrained(
+    "./HaiJava-Surgeon-Qwen2.5-Coder-7B-SFT-v1",
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+# Apply new LoRA for specialized training
+lora_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    target_modules=["q_proj", "v_proj", "k_proj", "o_proj"],  # Can expand targets
+    lora_dropout=0.05,
+    task_type="CAUSAL_LM"
+)
+
+model = get_peft_model(base_model, lora_config)
+
+# Continue training...
+```
+
+---
+
+## 📊 Comparison with Alternatives
+
+| Model | Exact Match | Pros | Cons |
+|-------|-------------|------|------|
+| **Base Model** | 9/50 (18%) | General purpose | Lower accuracy on Java bugs |
+| **Base + LoRA Adapter** | 14/50 (28%) | Modular, smaller files | Requires adapter loading |
+| **This Merged Model** | 14/50 (28%) | ✅ Fast loading<br/>✅ Simple deployment<br/>✅ Ready for more training | Larger file size (~14GB) |
+
+---
+
+## ⚠️ Known Limitations
+
+Based on evaluation, this model still struggles with:
+- **API Misuse Detection** (0% accuracy)
+- **Edge Case Handling** (0% accuracy)
+- **Null Pointer Exception Fixes** (0% accuracy)
+- **Python Bug Fixing** (0% accuracy on OOD samples)
+
+**Recommendation**: Continue training with more diverse samples focusing on these categories.
+
+---
+
+## 📚 Related Files
+
+- **Evaluation Report**: `../local_inference/CHECKPOINT_COMPARISON_54_vs_1000.md`
+- **Original LoRA Checkpoint**: `../checkpoint-1000/`
+- **Merge Script**: `../merge_lora_to_base.py`
+- **Evaluation Results**: `../local_inference/evaluation_results_sequential_*.json`
+
+---
+
+## 🔄 Version History
+
+| Version | Date | Description |
+|---------|------|-------------|
+| v1.0 | 2026-01-02 | Initial merge of checkpoint-1000 into base model |
+
+---
+
+## 📝 License
+
+Inherits license from base model: Qwen/Qwen2.5-Coder-7B-Instruct
+
+---
+
+## 🙏 Acknowledgments
+
+- **Base Model**: Qwen Team (Alibaba Cloud)
+- **Fine-tuning Framework**: LLaMA-Factory
+- **Evaluation Framework**: Custom 50-sample test suite
+
+---
+
+**For questions or issues, refer to the evaluation documentation in `local_inference/`**
+
+

added_tokens.json

@@ -0,0 +1,24 @@
+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
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+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

@@ -0,0 +1,54 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# 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' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.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' }}
+{%- endif %}

config.json

@@ -0,0 +1,28 @@
+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": 131072,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.52.4",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.1,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.52.4"
+}

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