---
language:
- en
license: apache-2.0
tags:
- zig
- code
- programming
- lora
- qwen2.5-coder
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
model_type: qwen2.5
library_name: transformers
---

# ZigNet Qwen2.5-Coder-7B

**Fine-tuned Qwen2.5-Coder-7B for Zig programming language analysis and assistance**

This model is part of the [ZigNet](https://github.com/fulgidus/zignet) project - an MCP (Model Context Protocol) server that provides intelligent Zig code analysis for Claude and other LLMs.

## Model Details

- **Base Model**: [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct)
- **Fine-tuning Method**: QLoRA (4-bit quantization)
- **Training Data**: 13,756 Zig code examples from official documentation (v0.13-0.15)
- **Supported Zig Versions**: 0.13.x, 0.14.x, 0.15.x
- **Training Hardware**: NVIDIA RTX 3090 (24GB VRAM)
- **Adapter Size**: ~155MB (LoRA adapters only)

## Training Configuration

```python
LoraConfig:
  - r: 16
  - lora_alpha: 32
  - target_modules: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
  - lora_dropout: 0.05
  - bias: "none"

TrainingArguments:
  - num_train_epochs: 3
  - per_device_train_batch_size: 16
  - learning_rate: 2e-4
  - warmup_steps: 100
  - fp16: true
```

## Dataset

The model was trained on a curated dataset of Zig examples including:
- Official Zig documentation examples (v0.13, v0.14, v0.15)
- Advanced features: comptime, generics, error handling, async
- Real-world code patterns from popular Zig projects

**Dataset**: [fulgidus/zignet-training-dataset](https://huggingface.co/datasets/fulgidus/zignet-training-dataset)

## Intended Use

This model is designed to:
- 📖 Provide Zig documentation context
- 💡 Suggest intelligent code fixes for Zig errors
- 🔍 Explain Zig-specific idioms and patterns
- ⚡ Generate idiomatic Zig code

**Note**: This model is NOT used for parsing or validation (handled by deterministic compiler-based tools). It focuses on documentation lookup and intelligent suggestions.

## Performance

- **Quality**: ⭐⭐⭐⭐⭐ Best-in-class for Zig syntax and idioms
- **Benchmarks**: 100% pass rate on Zig validation tests
- **Response Time**: ~15-20s (after GGUF quantization)

## Usage

### With Transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-Coder-7B-Instruct",
    torch_dtype=torch.float16,
    device_map="auto"
)

# Load LoRA adapters
model = PeftModel.from_pretrained(base_model, "fulgidus/zignet-qwen2.5-coder-7b")

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-7B-Instruct")

# Generate
prompt = "Explain Zig comptime feature with an example"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=500)
print(tokenizer.decode(outputs[0]))
```

### With ZigNet MCP Server

This model is integrated into ZigNet for use with Claude:

```bash
# Install ZigNet
git clone https://github.com/fulgidus/zignet
cd zignet
pnpm install
pnpm run build

# Configure MCP client (Claude Desktop)
# Add to ~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "zignet": {
      "command": "node",
      "args": ["/path/to/zignet/dist/mcp-server.js"]
    }
  }
}
```

## Limitations

- Focused on Zig 0.13-0.15 (may have limited accuracy on very old or very new syntax)
- LoRA adapters only (requires base model for inference)
- Optimized for English documentation and comments
- Not suitable for real-time parsing (use ZigNet's AST parser for that)

## Citation

```bibtex
@software{zignet2025,
  author = {fulgidus},
  title = {ZigNet: Intelligent Zig Code Analysis via MCP},
  year = {2025},
  url = {https://github.com/fulgidus/zignet}
}
```

## License

Apache-2.0 (same as base model)

## Acknowledgments

- **Base Model**: [Qwen2.5-Coder](https://github.com/QwenLM/Qwen2.5-Coder) by Alibaba Cloud
- **Zig Language**: [ziglang.org](https://ziglang.org)
- **Training Framework**: HuggingFace Transformers + PEFT

---

**Project**: [github.com/fulgidus/zignet](https://github.com/fulgidus/zignet)  
**Author**: fulgidus  
**Date**: October 2025