File size: 2,268 Bytes
3dbf962 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 |
---
license: apache-2.0
tags:
- code-review
- javascript
- mlx
- gguf
- qwen2.5-coder
base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
---
# AI Code Review Model - Javascript
This is a fine-tuned code review model specialized for **Javascript** code analysis.
## Model Details
- **Base Model**: Qwen/Qwen2.5-Coder-1.5B-Instruct
- **Training Method**: LoRA fine-tuning with MLX
- **Format**: GGUF (Q4_K_M quantization)
- **Target Language**: Javascript
- **Purpose**: Automated code review for CI/CD pipelines
## Usage
### Docker (Recommended)
```bash
docker pull ghcr.io/iq2i/ai-code-review:javascript-latest
docker run --rm -v $(pwd):/workspace ghcr.io/iq2i/ai-code-review:javascript-latest /workspace/src
```
### llama.cpp
```bash
# Download the model
wget https://huggingface.co/loicsapone/ai-code-review-javascript/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": [
{
"type": "bug",
"severity": "medium",
"line": 42,
"description": "Potential null pointer",
"suggestion": "Add null check"
}
],
"positive_points": [
"Good error handling",
"Clear variable names"
]
}
```
## Training
This model was trained on curated Javascript code review examples using:
- MLX framework for Apple Silicon acceleration
- LoRA adapters (r=8, alpha=16)
- Custom dataset of real-world code issues
For training details, see the [GitHub repository](https://github.com/iq2i/ai-code-review).
## Limitations
- Optimized for Javascript syntax and best practices
- May not catch all edge cases or security vulnerabilities
- Should be used as a supplementary tool, not a replacement for human review
## License
Apache 2.0
## Citation
```bibtex
@software{ai_code_review_javascript,
title = {AI Code Review Model for Javascript},
author = {IQ2i Team},
year = {2025},
url = {https://github.com/iq2i/ai-code-review}
}
```
|