---
title: Mistral-Large-4-1T-A52B-Text-2bit
canonical_url: "https://www.modelscope.cn/models/mlx-community/Mistral-Large-4-1T-A52B-Text-2bit"
md_url: "https://www.modelscope.cn/models/mlx-community/Mistral-Large-4-1T-A52B-Text-2bit.md"
repository: mlx-community/Mistral-Large-4-1T-A52B-Text-2bit
last_updated: 2026-10-30
license: other
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - mistral_large4
architectures:
  - MistralLarge4ForConditionalGeneration
base_model:
  - mistralai/Mistral-Large-4-1T-A52B
base_model_relation: quantized
parameters: 101.0B
tensor_type:
  - BF16
  - U32
library_name:
  - mlx
  - safetensors
downloads: 12
stars: 0
tags:
  - mlx
---

# Mistral-Large-4-1T-A52B-Text-2bit

> Mistral-Large-4-1T-A52B-Text-2bit - mlx-community 在 ModelScope 开源的模型。mlx-community/Mistral-Large-4-1T-A52B-Text-2bit

mlx-community/Mistral-Large-4-1T-A52B-Text-2bit 是 ModelScope 魔搭社区上的 101.0B 参数text-generation模型，采用 other 许可，基于 mistralai/Mistral-Large-4-1T-A52B 构建。

- **Repository**: mlx-community/Mistral-Large-4-1T-A52B-Text-2bit
- **License**: other
- **Tasks**: text-generation
- **Parameters**: 101.0B
- **Base model**: mistralai/Mistral-Large-4-1T-A52B
- **Tags**: mlx
- **Downloads**: 12
- **Stars**: 0
- **Last updated**: 2026-10-30

Source: https://www.modelscope.cn/models/mlx-community/Mistral-Large-4-1T-A52B-Text-2bit

---

# mlx-community/Mistral-Large-4-1T-A52B-Text-2bit

This model [mlx-community/Mistral-Large-4-1T-A52B-Text-2bit](https://huggingface.co/mlx-community/Mistral-Large-4-1T-A52B-Text-2bit) was converted to MLX format from [mistralai/Mistral-Large-4-1T-A52B](https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B) using mlx-lm with [ml-explore/mlx-lm#2018](https://github.com/ml-explore/mlx-lm/pull/2018). Refer to the [original model card](https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B) for more details on the model.

This is a text-only conversion. The vision encoder and the DSpark draft head are not included.

The FP8 weights were dequantized and then quantized to 2-bit affine for the routed experts and 6-bit affine for the other layers, with group size 64 (2.58 bits per weight). The router and two indexer projections stay in bf16, as in the original checkpoint. Peak memory was about 338 GB in a short generation test.

Tested with mlx-lm 0.32.3.dev13 and MLX 0.32.4.

## Use with mlx

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

```python
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/Mistral-Large-4-1T-A52B-Text-2bit")

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)
```