Instructions to use Safeeq/pycoder-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Safeeq/pycoder-gguf 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 Safeeq/pycoder-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf Safeeq/pycoder-gguf:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Safeeq/pycoder-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf Safeeq/pycoder-gguf:Q8_0

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 Safeeq/pycoder-gguf:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Safeeq/pycoder-gguf:Q8_0

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 Safeeq/pycoder-gguf:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Safeeq/pycoder-gguf:Q8_0

Use Docker

docker model run hf.co/Safeeq/pycoder-gguf:Q8_0

LM Studio
Jan
Ollama
How to use Safeeq/pycoder-gguf with Ollama:
```
ollama run hf.co/Safeeq/pycoder-gguf:Q8_0
```
Unsloth Desktop

How to use Safeeq/pycoder-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Safeeq/pycoder-gguf:Q8_0

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": "Safeeq/pycoder-gguf:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Safeeq/pycoder-gguf with Docker Model Runner:
```
docker model run hf.co/Safeeq/pycoder-gguf:Q8_0
```

Lemonade

How to use Safeeq/pycoder-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Safeeq/pycoder-gguf:Q8_0

Run and chat with the model

lemonade run user.pycoder-gguf-Q8_0

List all available models

lemonade list

Hermes Agent

How to use Safeeq/pycoder-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Safeeq/pycoder-gguf:Q8_0

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 Safeeq/pycoder-gguf:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Safeeq/pycoder-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Safeeq/pycoder-gguf:Q8_0

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 "Safeeq/pycoder-gguf:Q8_0" \
  --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"

PyCoder

PyCoder is a Qwen2.5-0.5B-Instruct fine-tune for Python code generation. This repository includes the merged model in Q8_0 GGUF format, an Ollama Modelfile, a small Ollama API client, and the source scripts used for training.

Use with Ollama

The quickest option is to install Ollama and run:

ollama run hf.co/Safeeq/pycoder-gguf:Q8_0

To download the files and create a local Ollama model instead:

hf download Safeeq/pycoder-gguf --local-dir pycoder-gguf
cd pycoder-gguf
ollama create pycoder -f Modelfile
ollama run pycoder

The hf command is provided by huggingface_hub (pip install huggingface_hub). Alternatively, download pycoder-q8_0.gguf and Modelfile from the Files and versions tab, keep them in the same directory, and run the ollama create command there.

Use the Python helper

The included run_pycoder.py sends a prompt to a local Ollama server. First create or run the pycoder model using the steps above, then run:

python run_pycoder.py "Write a function that reverses a string"

The helper uses only the Python standard library. Ollama must be installed and running locally.

Model behavior and limitations

Intended for generating Python code. For unrelated requests it is trained to return # ERROR: Only Python code requests are supported.
This behavior is learned, not a security boundary; prompts can still produce other content.
This is a small 0.5B-parameter model. Outputs can be incorrect, incomplete, or invalid Python, and no formal benchmark results are provided here.
Review and test all generated code. Do not run untrusted output directly; use an isolated environment when executing generated code.

Training

The model was fine-tuned from Qwen/Qwen2.5-0.5B-Instruct with LoRA. make_dataset.py prepares examples based on iamtarun/python_code_instructions_18k_alpaca plus off-topic refusal examples. The training scripts and dependencies are included for reference; training requires a compatible environment and hardware. The generated data.jsonl, intermediate checkpoints, and merged full-precision model are not included.

Files

pycoder-q8_0.gguf: quantized model weights.
Modelfile: Ollama configuration for the local GGUF.
run_pycoder.py: small client for the local Ollama API.
common.py, make_dataset.py, train.py, merge.py, test_model.py: training and testing source.
requirements.txt: dependencies for the training scripts and upload helper.
LICENSE: license terms.

License

The files in this repository are provided under the Apache License 2.0. The model is based on Qwen/Qwen2.5-0.5B-Instruct; review the upstream model and dataset terms when redistributing or using their materials.

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GGUF

Model size

0.5B params

Architecture

qwen2

Hardware compatibility

8-bit

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