---
title: Hunyuan-0.5B-Instruct-8bit
canonical_url: "https://www.modelscope.cn/models/mlx-community/Hunyuan-0.5B-Instruct-8bit"
md_url: "https://www.modelscope.cn/models/mlx-community/Hunyuan-0.5B-Instruct-8bit.md"
repository: mlx-community/Hunyuan-0.5B-Instruct-8bit
last_updated: 2025-08-05
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - hunyuan_v1_dense
architectures:
  - HunYuanDenseV1ForCausalLM
base_model:
  - tencent/Hunyuan-0.5B-Instruct
base_model_relation: quantized
parameters: 151.6M
tensor_type:
  - U32
  - BF16
library_name:
  - mlx
  - safetensors
  - pytorch
frameworks:
  - pytorch
inference_backends:
  - "deploy_task text"
  - "sglang 0.5.2"
downloads: 867
stars: 0
tags:
  - mlx
---

# Hunyuan-0.5B-Instruct-8bit

> Hunyuan-0.5B-Instruct-8bit - mlx-community 在 ModelScope 开源的模型。mlx-community/Hunyuan-0.5B-Instruct-8bit

mlx-community/Hunyuan-0.5B-Instruct-8bit 是 ModelScope 魔搭社区上的 151.6M 参数text-generation模型，基于 tencent/Hunyuan-0.5B-Instruct 构建，可用 deploy_task text、sglang 0.5.2 部署。

- **Repository**: mlx-community/Hunyuan-0.5B-Instruct-8bit
- **Tasks**: text-generation
- **Parameters**: 151.6M
- **Base model**: tencent/Hunyuan-0.5B-Instruct
- **Inference backends**: deploy_task text, sglang 0.5.2
- **Tags**: mlx
- **Downloads**: 867
- **Stars**: 0
- **Last updated**: 2025-08-05

Source: https://www.modelscope.cn/models/mlx-community/Hunyuan-0.5B-Instruct-8bit

---

# mlx-community/Hunyuan-0.5B-Instruct-8bit

This model [mlx-community/Hunyuan-0.5B-Instruct-8bit](https://huggingface.co/mlx-community/Hunyuan-0.5B-Instruct-8bit) was
converted to MLX format from [tencent/Hunyuan-0.5B-Instruct](https://huggingface.co/tencent/Hunyuan-0.5B-Instruct)
using mlx-lm version **0.26.2**.

## Use with mlx

```bash
pip install mlx-lm
```

```python
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/Hunyuan-0.5B-Instruct-8bit")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)
```