---
title: LLaDA2.2-flash-OptiQ-2bit
canonical_url: "https://www.modelscope.cn/models/latentnode/LLaDA2.2-flash-OptiQ-2bit"
md_url: "https://www.modelscope.cn/models/latentnode/LLaDA2.2-flash-OptiQ-2bit.md"
repository: latentnode/LLaDA2.2-flash-OptiQ-2bit
chinese_name: LLaDA2.2-flash-OptiQ-2bit
last_updated: 2026-07-27
license: apache-2.0
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - llada2_moe
architectures:
  - LLaDA2MoeModelLM
base_model:
  - inclusionAI/LLaDA2.2-flash
base_model_relation: quantized
parameters: 11.3B
tensor_type:
  - U32
  - BF16
library_name:
  - mlx
  - safetensors
language:
  - en
downloads: 11
stars: 0
tags:
  - mlx
  - optiq
  - diffusion
---

# LLaDA2.2-flash-OptiQ-2bit

> LLaDA2.2-flash-OptiQ-2bit - latentnode 在 ModelScope 开源的模型。LLaDA2.2-flash-OptiQ-2bit

latentnode/LLaDA2.2-flash-OptiQ-2bit 是 ModelScope 魔搭社区上的 11.3B 参数text-generation模型，采用 apache-2.0 许可，基于 inclusionAI/LLaDA2.2-flash 构建。

- **Repository**: latentnode/LLaDA2.2-flash-OptiQ-2bit
- **License**: apache-2.0
- **Tasks**: text-generation
- **Parameters**: 11.3B
- **Base model**: inclusionAI/LLaDA2.2-flash
- **Tags**: mlx, optiq, diffusion
- **Downloads**: 11
- **Stars**: 0
- **Last updated**: 2026-07-27

Source: https://www.modelscope.cn/models/latentnode/LLaDA2.2-flash-OptiQ-2bit

---

# LLaDA2.2-flash-OptiQ-2bit

> **Built with [mlx-optiq](https://mlx-optiq.com)**, the MLX-native toolkit to
> quantize, fine-tune, and serve LLMs locally on Apple Silicon (no PyTorch, no
> cloud). [Try the Lab](https://mlx-optiq.com/docs/lab/) · [All OptiQ
> quants](https://mlx-optiq.com/models) · [Docs](https://mlx-optiq.com/docs/)

An [OptiQ](https://mlx-optiq.com) mixed-precision MLX quant of
**LLaDA2.2-flash**, a ~100B **diffusion** language model with a 256-routed-expert
sparse MoE. This is an extreme 2-bit build.

- **Mixed 2/4-bit `static` build** — per-layer bit-widths assigned to a 2.5
  target bits-per-weight.
- **192 GB bf16 to 36 GB on disk** (5.3x).

## Requirements

Needs `optiq >= 0.4.4`, which ships the vendored `llada2_moe` decoder (the
256-expert diffusion MoE) and the block-diffusion decode loop. Stock `mlx-lm`
has no `llada2_moe` arch and cannot load or generate from this repo.

```bash
pip install -U optiq
```

## Running it

LLaDA2 is a masked-diffusion model, not autoregressive — it denoises a canvas
block by block. `optiq serve` detects the arch and routes it through OptiQ's
vendored decoder and the block-diffusion decode loop automatically:

```bash
optiq serve --model mlx-community/LLaDA2.2-flash-OptiQ-2bit
```

Then call the OpenAI-compatible endpoint at `http://localhost:8000/v1`, or use
it from the [OptiQ Lab](https://mlx-optiq.com/docs/lab/) and `optiq code`.