Instructions to use WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF", dtype="auto")

llama-cpp-python

How to use WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF",
	filename="Hunyuan-PythonGOD-0.5B.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF:Q4_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 WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF:Q4_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 WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF:Q4_K_M

SGLang

How to use WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF with Ollama:
```
ollama run hf.co/WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF 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 WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF 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 WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF to start chatting

Pi new

How to use WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF:Q4_K_M

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": "Hunyuan-Python.GOD-0.5B-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF with Docker Model Runner:
```
docker model run hf.co/WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF:Q4_K_M
```

Lemonade

How to use WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Hunyuan-Python.GOD-0.5B-GGUF-Q4_K_M

List all available models

lemonade list

Hunyuan-PythonGOD-0.5B-GGUF

Hunyuan-PythonGOD-0.5B-GGUF is a compact Python-specialized coding model released in GGUF format for lightweight local inference. It is derived from a full fine-tune of tencent/Hunyuan-0.5B-Instruct and is aimed at code generation, Python scripting, debugging help, implementation tasks, and coding-oriented chat workflows.

This repo provides quantized GGUF builds for efficient use with llama.cpp-compatible runtimes and other GGUF-serving backends.

Model Details

Base Model

Base model: tencent/Hunyuan-0.5B-Instruct
Architecture: Causal decoder-only language model
Parameter scale: ~0.5B
Specialization: Python coding and general code-assistant behavior
Release format: GGUF

Included Files

Hunyuan-PythonGOD-0.5B.Q4_K_M.gguf
Hunyuan-PythonGOD-0.5B.Q5_K_M.gguf
Hunyuan-PythonGOD-0.5B.f16.gguf

Training Summary

This GGUF release is based on a full fine-tune, not an adapter-only export.

Training Datasets

WithinUsAI/Python_GOD_Coder_Omniforge_AI_12k
WithinUsAI/Python_GOD_Coder_5k
WithinUsAI/Legend_Python_CoderV.1

Training Characteristics

Full-parameter fine-tuning
Python/code-oriented instruction tuning
Exported as standard model weights before GGUF conversion
Intended for compact coding assistance and local inference experimentation

Intended Uses

Good Fits

Python function generation
Python script writing
Debugging assistance
Automation script drafting
Code-oriented local assistants
Small-model coding experiments

Not Intended For

Safety-critical software deployment without review
Autonomous execution without sandboxing
Guaranteed bug-free or secure code generation
Medical, legal, or financial decision support

Quantization Notes

This repo includes multiple tradeoff points:

Q4_K_M: smaller footprint, faster/lighter inference
Q5_K_M: stronger quality-to-size balance
F16: highest fidelity in this repo, larger memory cost

Example llama.cpp Usage

./llama-cli -m Hunyuan-PythonGOD-0.5B.Q5_K_M.gguf -p "Write a Python function that validates an email address." -n 256

Downloads last month: 493

GGUF

Model size

0.5B params

Architecture

hunyuan-dense

Hardware compatibility

4-bit

5-bit

16-bit

Model tree for WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF

Base model

tencent/Hunyuan-0.5B-Pretrain

Finetuned

tencent/Hunyuan-0.5B-Instruct

Quantized

(16)

this model

Datasets used to train WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF

Collection including WithinUsAI/Hunyuan-Python.GOD-0.5B-GGUF

WithIn US AI (((GGUF MODELS)))

Collection

LLM MODELS TRAINED, FINE-TUNED, MERGED BY (WITHIN US AI) • 19 items • Updated 3 days ago • 5