---
title: LongCat-Flash-Thinking-2601-4bit
canonical_url: "https://www.modelscope.cn/models/mlx-community/LongCat-Flash-Thinking-2601-4bit"
md_url: "https://www.modelscope.cn/models/mlx-community/LongCat-Flash-Thinking-2601-4bit.md"
repository: mlx-community/LongCat-Flash-Thinking-2601-4bit
last_updated: 2026-01-18
license: mit
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - longcat_flash
architectures:
  - LongcatCausalLM
base_model:
  - meituan-longcat/LongCat-Flash-Thinking-2601
base_model_relation: quantized
parameters: 86.4B
tensor_type:
  - U32
  - BF16
  - F32
library_name:
  - mlx
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 26
stars: 0
tags:
  - transformers
  - mlx
---

# LongCat-Flash-Thinking-2601-4bit

> LongCat-Flash-Thinking-2601-4bit - mlx-community 在 ModelScope 开源的模型。mlx-community/LongCat-Flash-Thinking-2601-4bit

mlx-community/LongCat-Flash-Thinking-2601-4bit 是 ModelScope 魔搭社区上的 86.4B 参数text-generation模型，采用 mit 许可，基于 meituan-longcat/LongCat-Flash-Thinking-2601 构建。

- **Repository**: mlx-community/LongCat-Flash-Thinking-2601-4bit
- **License**: mit
- **Tasks**: text-generation
- **Parameters**: 86.4B
- **Base model**: meituan-longcat/LongCat-Flash-Thinking-2601
- **Tags**: transformers, mlx
- **Downloads**: 26
- **Stars**: 0
- **Last updated**: 2026-01-18

Source: https://www.modelscope.cn/models/mlx-community/LongCat-Flash-Thinking-2601-4bit

---

# mlx-community/LongCat-Flash-Thinking-2601-4bit

This model [mlx-community/LongCat-Flash-Thinking-2601-4bit](https://huggingface.co/mlx-community/LongCat-Flash-Thinking-2601-4bit) was
converted to MLX format from [meituan-longcat/LongCat-Flash-Thinking-2601](https://huggingface.co/meituan-longcat/LongCat-Flash-Thinking-2601)
using mlx-lm version **0.30.2**.

## Use with mlx

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

```python
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/LongCat-Flash-Thinking-2601-4bit")

prompt = "hello"

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

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