---
title: QwQ-Coder-instruct-mlx-4Bit
canonical_url: "https://www.modelscope.cn/models/mlx-community/QwQ-Coder-instruct-mlx-4Bit"
md_url: "https://www.modelscope.cn/models/mlx-community/QwQ-Coder-instruct-mlx-4Bit.md"
repository: mlx-community/QwQ-Coder-instruct-mlx-4Bit
last_updated: 2025-03-20
license: apache-2.0
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - qwen2
architectures:
  - Qwen2ForCausalLM
parameters: 5.1B
tensor_type:
  - F16
  - U32
library_name:
  - mlx
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
  - zh
inference_backends:
  - "deploy_task text/emb"
  - "lmdeploy 0.9.1"
  - "lmdeploy_turbomind 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 523
stars: 1
tags:
  - merge
  - mlx
  - mlx-my-repo
---

# QwQ-Coder-instruct-mlx-4Bit

> QwQ-Coder-instruct-mlx-4Bit - mlx-community 在 ModelScope 开源的模型。bobig/QwQ-Coder-instruct-mlx-4Bit

mlx-community/QwQ-Coder-instruct-mlx-4Bit 是 ModelScope 魔搭社区上的 5.1B 参数text-generation模型，采用 apache-2.0 许可，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: mlx-community/QwQ-Coder-instruct-mlx-4Bit
- **License**: apache-2.0
- **Tasks**: text-generation
- **Parameters**: 5.1B
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Tags**: merge, mlx, mlx-my-repo
- **Downloads**: 523
- **Stars**: 1
- **Last updated**: 2025-03-20

Source: https://www.modelscope.cn/models/mlx-community/QwQ-Coder-instruct-mlx-4Bit

---

# bobig/QwQ-Coder-instruct-mlx-4Bit

This is pretty good.  QwQ brains and memory + Qwen code instruct

Now in delicious MLX, eat it or wear it

32k context is solid in QwQ:  https://fiction.live/stories/Fiction-liveBench-Mar-14-2025/oQdzQvKHw8JyXbN87

Test Prompt: Write a quick sort in C++

The Model [bobig/QwQ-Coder-instruct-mlx-4Bit](https://huggingface.co/bobig/QwQ-Coder-instruct-mlx-4Bit) was converted to MLX format from [YOYO-AI/QwQ-Coder-instruct](https://huggingface.co/YOYO-AI/QwQ-Coder-instruct) 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("bobig/QwQ-Coder-instruct-mlx-4Bit")

prompt="hello"

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

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