Instructions to use fulgidus/zignet-qwen2.5-coder-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fulgidus/zignet-qwen2.5-coder-7b with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("fulgidus/zignet-qwen2.5-coder-7b", dtype="auto")

llama-cpp-python

How to use fulgidus/zignet-qwen2.5-coder-7b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="fulgidus/zignet-qwen2.5-coder-7b",
	filename="gguf/zignet-qwen-7b-q4km.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use fulgidus/zignet-qwen2.5-coder-7b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf fulgidus/zignet-qwen2.5-coder-7b
# Run inference directly in the terminal:
llama-cli -hf fulgidus/zignet-qwen2.5-coder-7b

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf fulgidus/zignet-qwen2.5-coder-7b
# Run inference directly in the terminal:
llama-cli -hf fulgidus/zignet-qwen2.5-coder-7b

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf fulgidus/zignet-qwen2.5-coder-7b
# Run inference directly in the terminal:
./llama-cli -hf fulgidus/zignet-qwen2.5-coder-7b

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf fulgidus/zignet-qwen2.5-coder-7b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf fulgidus/zignet-qwen2.5-coder-7b

Use Docker

docker model run hf.co/fulgidus/zignet-qwen2.5-coder-7b

LM Studio
Jan
Ollama
How to use fulgidus/zignet-qwen2.5-coder-7b with Ollama:
```
ollama run hf.co/fulgidus/zignet-qwen2.5-coder-7b
```

Unsloth Studio new

How to use fulgidus/zignet-qwen2.5-coder-7b with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for fulgidus/zignet-qwen2.5-coder-7b to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for fulgidus/zignet-qwen2.5-coder-7b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for fulgidus/zignet-qwen2.5-coder-7b to start chatting

Pi new

How to use fulgidus/zignet-qwen2.5-coder-7b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf fulgidus/zignet-qwen2.5-coder-7b

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "fulgidus/zignet-qwen2.5-coder-7b"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use fulgidus/zignet-qwen2.5-coder-7b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf fulgidus/zignet-qwen2.5-coder-7b

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default fulgidus/zignet-qwen2.5-coder-7b

Run Hermes

hermes

Docker Model Runner
How to use fulgidus/zignet-qwen2.5-coder-7b with Docker Model Runner:
```
docker model run hf.co/fulgidus/zignet-qwen2.5-coder-7b
```

Lemonade

How to use fulgidus/zignet-qwen2.5-coder-7b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull fulgidus/zignet-qwen2.5-coder-7b

Run and chat with the model

lemonade run user.zignet-qwen2.5-coder-7b-{{QUANT_TAG}}

List all available models

lemonade list

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 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
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

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

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

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:

# 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

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