README.md

metadata

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

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