---
title: FuseO1-DeepSeekR1-Qwen2.5-Coder-7B-Preview-q4
canonical_url: "https://www.modelscope.cn/models/mlx-community/FuseO1-DeepSeekR1-Qwen2.5-Coder-7B-Preview-q4"
md_url: "https://www.modelscope.cn/models/mlx-community/FuseO1-DeepSeekR1-Qwen2.5-Coder-7B-Preview-q4.md"
repository: mlx-community/FuseO1-DeepSeekR1-Qwen2.5-Coder-7B-Preview-q4
last_updated: 2025-03-12
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - qwen2
architectures:
  - Qwen2ForCausalLM
parameters: 1.2B
tensor_type:
  - U32
  - F16
library_name:
  - mlx
  - safetensors
  - pytorch
frameworks:
  - pytorch
inference_backends:
  - "deploy_task text/emb"
  - "lmdeploy 0.9.1"
  - "lmdeploy_turbomind 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 551
stars: 0
tags:
  - mergekit
  - merge
  - mlx
---

# FuseO1-DeepSeekR1-Qwen2.5-Coder-7B-Preview-q4

> FuseO1-DeepSeekR1-Qwen2.5-Coder-7B-Preview-q4 - mlx-community 在 ModelScope 开源的模型。mlx-community/FuseO1-DeepSeekR1-Qwen2.5-Coder-7B-Preview-q4

mlx-community/FuseO1-DeepSeekR1-Qwen2.5-Coder-7B-Preview-q4 是 ModelScope 魔搭社区上的 1.2B 参数text-generation模型，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: mlx-community/FuseO1-DeepSeekR1-Qwen2.5-Coder-7B-Preview-q4
- **Tasks**: text-generation
- **Parameters**: 1.2B
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Tags**: mergekit, merge, mlx
- **Downloads**: 551
- **Stars**: 0
- **Last updated**: 2025-03-12

Source: https://www.modelscope.cn/models/mlx-community/FuseO1-DeepSeekR1-Qwen2.5-Coder-7B-Preview-q4

---

# mlx-community/FuseO1-DeepSeekR1-Qwen2.5-Coder-7B-Preview-q4

The Model [mlx-community/FuseO1-DeepSeekR1-Qwen2.5-Coder-7B-Preview-q4](https://huggingface.co/mlx-community/FuseO1-DeepSeekR1-Qwen2.5-Coder-7B-Preview-q4) was
converted to MLX format from [sealad886/FuseO1-DeepSeekR1-Qwen2.5-Coder-7B-Preview](https://huggingface.co/sealad886/FuseO1-DeepSeekR1-Qwen2.5-Coder-7B-Preview)
using mlx-lm version **0.21.5**.

## Use with mlx

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

```python
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/FuseO1-DeepSeekR1-Qwen2.5-Coder-7B-Preview-q4")

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)
```