Update model card for multi-language model

README.md

@@ -1,3 +1,111 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+- code-review
+- multi-language
+- mlx
+- gguf
+- qwen2.5-coder
+base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
+---
+
+# AI Code Review Model
+
+Multi-language code review model optimized for automated code review in CI/CD pipelines.
+
+## Model Details
+
+- **Base Model**: Qwen/Qwen2.5-Coder-1.5B-Instruct
+- **Training Method**: LoRA fine-tuning with MLX
+- **Format**: GGUF (Q4_K_M quantization)
+- **Purpose**: Automated code review for CI/CD pipelines
+
+## Usage
+
+### Docker (Recommended)
+
+```bash
+docker pull ghcr.io/iq2i/ai-code-review:latest
+
+# Review your codebase
+docker run --rm -v $(pwd):/workspace ghcr.io/iq2i/ai-code-review:latest /workspace/src
+```
+
+### llama.cpp
+
+```bash
+# Download the model
+wget https://huggingface.co/iq2i/ai-code-review/resolve/main/model-Q4_K_M.gguf
+
+# Run inference
+./llama-cli -m model-Q4_K_M.gguf -p "Review this code: ..."
+```
+
+### Python (llama-cpp-python)
+
+```python
+from llama_cpp import Llama
+
+llm = Llama(model_path="model-Q4_K_M.gguf")
+output = llm("Review this code: ...", max_tokens=512)
+print(output)
+```
+
+## Output Format
+
+The model outputs JSON structured code reviews:
+
+```json
+{
+  "summary": "Brief overview of code quality",
+  "score": 8,
+  "issues": [
+    {
+      "category": "security",
+      "severity": "medium",
+      "title": "Potential SQL injection",
+      "description": "User input not sanitized",
+      "suggestion": {
+        "type": "code_change",
+        "content": "Use prepared statements"
+      }
+    }
+  ],
+  "positive_points": [
+    {
+      "title": "Good error handling",
+      "description": "Comprehensive try-catch blocks"
+    }
+  ]
+}
+```
+
+## Training
+
+- **Training examples**: 70+ real-world code issues
+- **Framework**: MLX for Apple Silicon acceleration
+- **Method**: LoRA adapters (r=4, alpha=8)
+- **Iterations**: 200
+
+For training details, see the [GitHub repository](https://github.com/iq2i/ai-code-review).
+
+## Limitations
+
+- Should be used as a supplementary tool, not a replacement for human review
+- May not catch all edge cases or security vulnerabilities
+- Best results on common programming patterns and frameworks
+
+## License
+
+Apache 2.0
+
+## Citation
+
+```bibtex
+@software{ai_code_review,
+  title = {AI Code Review Model},
+  author = {IQ2i Team},
+  year = {2025},
+  url = {https://github.com/iq2i/ai-code-review}
+}
+```