---
title: llava-interleave-qwen-0.5b-6bit
canonical_url: "https://www.modelscope.cn/models/mlx-community/llava-interleave-qwen-0.5b-6bit"
md_url: "https://www.modelscope.cn/models/mlx-community/llava-interleave-qwen-0.5b-6bit.md"
repository: mlx-community/llava-interleave-qwen-0.5b-6bit
last_updated: 2025-03-27
license: other
pipeline_tag: image-text-to-text
tasks:
  - image-text-to-text
model_type:
  - llava
architectures:
  - LlavaForConditionalGeneration
parameters: 499.8M
tensor_type:
  - F16
  - U32
library_name:
  - mlx
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
inference_backends:
  - "deploy_task vlm/text/emb"
  - "lmdeploy 0.9.1"
  - "lmdeploy_turbomind 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 464
stars: 0
tags:
  - vision
  - image-text-to-text
  - mlx
---

# llava-interleave-qwen-0.5b-6bit

> llava-interleave-qwen-0.5b-6bit - mlx-community 在 ModelScope 开源的模型。mlx-community/llava-interleave-qwen-0.5b-6bit This model was converted to MLX format from llava-hf/llava-interleave-qwen-0.5b-hf using mlx-vlm version 0.1.21. Refer to the original model card…

mlx-community/llava-interleave-qwen-0.5b-6bit 是 ModelScope 魔搭社区上的 499.8M 参数image-text-to-text模型，采用 other 许可，可用 deploy_task vlm/text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: mlx-community/llava-interleave-qwen-0.5b-6bit
- **License**: other
- **Tasks**: image-text-to-text
- **Parameters**: 499.8M
- **Inference backends**: deploy_task vlm/text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Tags**: vision, image-text-to-text, mlx
- **Downloads**: 464
- **Stars**: 0
- **Last updated**: 2025-03-27

Source: https://www.modelscope.cn/models/mlx-community/llava-interleave-qwen-0.5b-6bit

---

# mlx-community/llava-interleave-qwen-0.5b-6bit
This model was converted to MLX format from [`llava-hf/llava-interleave-qwen-0.5b-hf`]() using mlx-vlm version **0.1.21**.
Refer to the [original model card](https://huggingface.co/llava-hf/llava-interleave-qwen-0.5b-hf) for more details on the model.
## Use with mlx

```bash
pip install -U mlx-vlm
```

```bash
python -m mlx_vlm.generate --model mlx-community/llava-interleave-qwen-0.5b-6bit --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
```