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-Python.GOD-0.5B-GGUF / README.md

gss1147

Update README.md

e28d316 verified about 2 months ago

preview code

raw

history blame contribute delete

2.67 kB

	---
	language:
	- en
	license: other
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- gguf
	- hunyuan
	- python
	- code-generation
	- code-assistant
	- instruct
	- conversational
	- causal-lm
	- full-finetune
	base_model:
	- tencent/Hunyuan-0.5B-Instruct
	datasets:
	- WithinUsAI/Python_GOD_Coder_Omniforge_AI_12k
	- WithinUsAI/Python_GOD_Coder_5k
	- WithinUsAI/Legend_Python_CoderV.1
	model-index:
	- name: Hunyuan-PythonGOD-0.5B-GGUF
	results: []
	---

	# 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

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