---
title: MiniCPM5-2B-8bit
canonical_url: "https://www.modelscope.cn/models/mlx-community/MiniCPM5-2B-8bit"
md_url: "https://www.modelscope.cn/models/mlx-community/MiniCPM5-2B-8bit.md"
repository: mlx-community/MiniCPM5-2B-8bit
last_updated: 2026-09-10
license: apache-2.0
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - llama
architectures:
  - LlamaForCausalLM
base_model:
  - openbmb/MiniCPM5-2B
base_model_relation: quantized
parameters: 708.0M
tensor_type:
  - BF16
  - U32
library_name:
  - mlx
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
  - zh
downloads: 38
stars: 0
tags:
  - minicpm
  - minicpm5
  - llama
  - text-generation
  - long-context
  - tool-calling
  - on-device
  - edge-ai
  - mlx
---

# MiniCPM5-2B-8bit

> MiniCPM5-2B-8bit - mlx-community 在 ModelScope 开源的模型。mlx-community/MiniCPM5-2B-8bit

mlx-community/MiniCPM5-2B-8bit 是 ModelScope 魔搭社区上的 708.0M 参数text-generation模型，采用 apache-2.0 许可，基于 openbmb/MiniCPM5-2B 构建。

- **Repository**: mlx-community/MiniCPM5-2B-8bit
- **License**: apache-2.0
- **Tasks**: text-generation
- **Parameters**: 708.0M
- **Base model**: openbmb/MiniCPM5-2B
- **Tags**: minicpm, minicpm5, llama, text-generation, long-context, tool-calling, on-device, edge-ai, mlx
- **Downloads**: 38
- **Stars**: 0
- **Last updated**: 2026-09-10

Source: https://www.modelscope.cn/models/mlx-community/MiniCPM5-2B-8bit

---

# mlx-community/MiniCPM5-2B-8bit

This model [mlx-community/MiniCPM5-2B-8bit](https://huggingface.co/mlx-community/MiniCPM5-2B-8bit) was
converted to MLX format from
[openbmb/MiniCPM5-2B](https://huggingface.co/openbmb/MiniCPM5-2B)
using mlx-lm version **0.31.3** (8-bit, group size 64).

## Use with mlx

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

```python
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/MiniCPM5-2B-8bit")

prompt = "Hello, who are you?"

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)
```