---
title: Hunyuan-PythonGOD-0.5B
canonical_url: "https://www.modelscope.cn/models/WithinUsAI/Hunyuan-PythonGOD-0.5B"
md_url: "https://www.modelscope.cn/models/WithinUsAI/Hunyuan-PythonGOD-0.5B.md"
repository: WithinUsAI/Hunyuan-PythonGOD-0.5B
last_updated: 2026-04-24
license: other
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - hunyuan_v1_dense
architectures:
  - HunYuanDenseV1ForCausalLM
base_model:
  - tencent/Hunyuan-0.5B-Instruct
base_model_relation: finetune
parameters: 539.0M
tensor_type:
  - F16
library_name:
  - pytorch
  - transformer
  - safetensors
frameworks:
  - pytorch
language:
  - en
inference_backends:
  - "deploy_task text"
  - "sglang 0.5.2"
downloads: 7
stars: 0
tags:
  - python
  - code-generation
  - code-assistant
  - causal-lm
  - full-finetune
  - hunyuan
  - transformers
  - safetensors
  - instruct
---

# Hunyuan-PythonGOD-0.5B

> Hunyuan-PythonGOD-0.5B - WithinUsAI 在 ModelScope 开源的模型。Hunyuan-PythonGOD-0.5B

WithinUsAI/Hunyuan-PythonGOD-0.5B 是 ModelScope 魔搭社区上的 539.0M 参数text-generation模型，采用 other 许可，基于 tencent/Hunyuan-0.5B-Instruct 构建，可用 deploy_task text、sglang 0.5.2 部署。

- **Repository**: WithinUsAI/Hunyuan-PythonGOD-0.5B
- **License**: other
- **Tasks**: text-generation
- **Parameters**: 539.0M
- **Base model**: tencent/Hunyuan-0.5B-Instruct
- **Inference backends**: deploy_task text, sglang 0.5.2
- **Tags**: python, code-generation, code-assistant, causal-lm, full-finetune, hunyuan, transformers, safetensors, instruct
- **Downloads**: 7
- **Stars**: 0
- **Last updated**: 2026-04-24

Source: https://www.modelscope.cn/models/WithinUsAI/Hunyuan-PythonGOD-0.5B

---

# Hunyuan-PythonGOD-0.5B

Hunyuan-PythonGOD-0.5B is a Python-focused full fine-tune of `tencent/Hunyuan-0.5B-Instruct`, built for code generation, coding assistance, implementation tasks, and instruction-following for Python-heavy workflows.

This release is intended as a compact coding model that keeps the small footprint of the 0.5B Hunyuan base while shifting its behavior toward practical Python generation and code-oriented responses.

## Model Details

### Model Description

- **Model name:** `gss1147/Hunyuan-PythonGOD-0.5B`
- **Base model:** `tencent/Hunyuan-0.5B-Instruct`
- **Architecture:** causal decoder-only language model
- **Model family tag:** `hunyuan_v1_dense`
- **Primary domain:** Python coding / coding assistant
- **Parameter count:** ~0.5B
- **Weights format:** safetensors
- **Tensor type in repo:** F16

### Developed by

- **Shared by:** `gss1147`

### Finetuned from model

- `tencent/Hunyuan-0.5B-Instruct`

## Intended Uses

### Direct Use

This model is intended for:

- Python function generation
- Python script writing
- debugging-oriented coding help
- implementation tasks
- code completion
- coding chat assistants
- lightweight local or cloud inference where a small coding model is preferred

### Downstream Use

Possible downstream uses include:

- code copilots
- coding bots
- Python tutoring helpers
- automation script generation
- benchmark experimentation for small code LLMs

### Out-of-Scope Use

This model is not designed for:

- safety-critical code deployment without human review
- medical, legal, or financial decision support
- secure production code without auditing
- autonomous execution pipelines without sandboxing
- guaranteed factual or bug-free code generation

## Training Details

### Training Objective

This model was trained as a **full fine-tune**, not as an adapter-only release.

Based on the training workflow you described and the run logs you shared, this release is meant to represent:

- **full-parameter fine-tuning**
- **no LoRA**
- **no QLoRA**
- **no PEFT adapters in the final model**
- **standard exported Hugging Face model weights**

### Training Data

This model was trained on the following datasets:

- `WithinUsAI/Python_GOD_Coder_Omniforge_AI_12k`
- `WithinUsAI/Python_GOD_Coder_5k`
- `WithinUsAI/Legend_Python_CoderV.1`

From the training logs you shared, the combined training corpus used:

- **11,760 rows** from `Python_GOD_Coder_Omniforge_AI_12k`
- **5,000 rows** from `Python_GOD_Coder_5k`
- **5,000 rows** from `Legend_Python_CoderV.1`

**Total rows:** **21,760**

### Training Procedure

From the training setup you shared, this model was trained with:

- **dual-GPU Kaggle training**
- **DeepSpeed-assisted distributed training**
- **full model fine-tuning**
- **evaluation during training**
- **final-save upload flow to Hugging Face**

### Sequence Length

- **Practical fine-tuning sequence length:** 4096 tokens

### Context Window Note

If the base model family exposes larger context metadata in config fields, that should not be taken as proof that the full fine-tuning run itself was performed at that larger length. This release should be treated as fine-tuned at **4096 tokens** unless revalidated separately.

## Evaluation

Formal benchmark results are not finalized in this card.

Benchmark attempts were made on free public coding benchmarks such as:

- HumanEval+
- MBPP+
- BigCodeBench-style workflows

However, based on the evaluation runs you shared, the harness setup encountered tool/runtime issues during some benchmark attempts, so this card does **not** claim final official benchmark scores yet.

### Observed Training Behavior

From the run logs you shared during training, the model showed:

- strong reduction in training loss over time
- strong reduction in eval loss over time
- stable continued learning well into the run
- increasingly code-specialized behavior relative to the base model

Examples from your shared eval progression included values around:

- ~0.2879 early in training
- ~0.1071
- ~0.0604
- ~0.0550
- ~0.0422
- ~0.0329
- ~0.0266
- ~0.0299
- ~0.0290

These are training/eval-run observations, not official public benchmark scores.

## How to Use

### Transformers

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "gss1147/Hunyuan-PythonGOD-0.5B"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    torch_dtype=torch.float16,
    device_map="auto",
)

prompt = "Write a Python function that merges overlapping intervals."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        do_sample=False,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))