Instructions to use dghf77/Qwen2.5-3B-Instruct.Q5_spec_Dot_code_generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use dghf77/Qwen2.5-3B-Instruct.Q5_spec_Dot_code_generation with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf dghf77/Qwen2.5-3B-Instruct.Q5_spec_Dot_code_generation:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf dghf77/Qwen2.5-3B-Instruct.Q5_spec_Dot_code_generation:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf dghf77/Qwen2.5-3B-Instruct.Q5_spec_Dot_code_generation:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf dghf77/Qwen2.5-3B-Instruct.Q5_spec_Dot_code_generation:Q5_K_M

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 dghf77/Qwen2.5-3B-Instruct.Q5_spec_Dot_code_generation:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf dghf77/Qwen2.5-3B-Instruct.Q5_spec_Dot_code_generation:Q5_K_M

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 dghf77/Qwen2.5-3B-Instruct.Q5_spec_Dot_code_generation:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dghf77/Qwen2.5-3B-Instruct.Q5_spec_Dot_code_generation:Q5_K_M

Use Docker

docker model run hf.co/dghf77/Qwen2.5-3B-Instruct.Q5_spec_Dot_code_generation:Q5_K_M

LM Studio
Jan
Ollama
How to use dghf77/Qwen2.5-3B-Instruct.Q5_spec_Dot_code_generation with Ollama:
```
ollama run hf.co/dghf77/Qwen2.5-3B-Instruct.Q5_spec_Dot_code_generation:Q5_K_M
```
Unsloth Desktop

How to use dghf77/Qwen2.5-3B-Instruct.Q5_spec_Dot_code_generation with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf dghf77/Qwen2.5-3B-Instruct.Q5_spec_Dot_code_generation:Q5_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "dghf77/Qwen2.5-3B-Instruct.Q5_spec_Dot_code_generation:Q5_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use dghf77/Qwen2.5-3B-Instruct.Q5_spec_Dot_code_generation with Docker Model Runner:
```
docker model run hf.co/dghf77/Qwen2.5-3B-Instruct.Q5_spec_Dot_code_generation:Q5_K_M
```

Lemonade

How to use dghf77/Qwen2.5-3B-Instruct.Q5_spec_Dot_code_generation with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dghf77/Qwen2.5-3B-Instruct.Q5_spec_Dot_code_generation:Q5_K_M

Run and chat with the model

lemonade run user.Qwen2.5-3B-Instruct.Q5_spec_Dot_code_generation-Q5_K_M

List all available models

lemonade list

Hermes Agent

How to use dghf77/Qwen2.5-3B-Instruct.Q5_spec_Dot_code_generation with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf dghf77/Qwen2.5-3B-Instruct.Q5_spec_Dot_code_generation:Q5_K_M

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 dghf77/Qwen2.5-3B-Instruct.Q5_spec_Dot_code_generation:Q5_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use dghf77/Qwen2.5-3B-Instruct.Q5_spec_Dot_code_generation with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf dghf77/Qwen2.5-3B-Instruct.Q5_spec_Dot_code_generation:Q5_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "dghf77/Qwen2.5-3B-Instruct.Q5_spec_Dot_code_generation:Q5_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Qwen2.5-3B-Instruct (LoRA DOT Fine-Tune, GGUF Q5)

A parameter-efficient fine-tune of Qwen2.5-3B-Instruct specialized for Graphviz DOT code generation.
This model was trained with LoRA adapters and exported in GGUF Q5_K_M quantization for lightweight, portable inference.

📌 Model Summary

Base Model: Qwen2.5-3B-Instruct (bnb-4bit)
Fine-tuning Method: LoRA (Low-Rank Adaptation)
Export Format: GGUF (llama.cpp-compatible)
Quantization: Q5_K_M (5-bit, medium precision)
File Size: 2.22 GB
License: Apache 2.0
Task: Natural language → Graphviz DOT code
Hosting: Hugging Face Hub

🚀 Motivation

The base Qwen2.5-3B-Instruct model often produced:

❌ Syntax errors (malformed nodes/edges, unclosed brackets, invalid attributes)
❌ Hallucinated content (extra nodes/edges not in the prompt)

Fine-tuning with LoRA adapters on a curated dataset of 671 compiler-validated DOT examples resolved these issues, ensuring structurally valid DOT syntax generation.

📂 Training Details

Dataset Source: Graphviz Gallery (paired natural-language prompts + DOT code)
Validation: Every DOT sample compiled successfully before inclusion
Final Corpus: ~671 bug-free instruction/output pairs
Format: Alpaca-style JSON (train.jsonl, val.jsonl)
Trainer: Hugging Face trl.SFTTrainer
Environment: Google Colab GPU runtime

⚙️ Technical Stack

Unsloth – optimized fine-tuning framework
Unsloth Zoo – pretrained configs + utilities
Tokenizer Utils – fixes for DOT-specific symbols
TRL – supervised fine-tuning trainer
Causal Convid + Mamba SSM – efficient sequence modeling for long DOT scripts
Ninja + shutil – build + file management utilities

📦 Repository Contents

Qwen2.5-3B-Instruct.Q5_K_M.gguf (2.22 GB) – quantized model weights
README.md – model card
.gitattributes – LFS configuration

🖥️ Inference & Deployment

Compatible runtimes:

llama.cpp (CLI / server)
llama-cpp-python
Ollama
LM Studio, Jan, Docker runners
Unsloth Studio, Lemonade

Example (llama.cpp CLI):

./main -m Qwen2.5-3B-Instruct.Q5_K_M.gguf -p "Generate a DOT diagram for a binary tree"

Downloads last month: 11

GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

5-bit

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