---
title: internlm-xcomposer-vl-7b
canonical_url: "https://www.modelscope.cn/models/Shanghai_AI_Laboratory/internlm-xcomposer-vl-7b"
md_url: "https://www.modelscope.cn/models/Shanghai_AI_Laboratory/internlm-xcomposer-vl-7b.md"
repository: Shanghai_AI_Laboratory/internlm-xcomposer-vl-7b
chinese_name: "浦语·灵笔-预训练-7B"
last_updated: 2025-02-26
license: apache-2.0
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - InternLMXComposer
architectures:
  - InternLMXComposerForCausalLM
library_name:
  - pytorch
frameworks:
  - pytorch
inference_backends:
  - "deploy_task text"
  - "lmdeploy 0.9.1"
downloads: 1503
stars: 4
---

# internlm-xcomposer-vl-7b

> internlm-xcomposer-vl-7b - Shanghai_AI_Laboratory 在 ModelScope 开源的模型。InternLM-XComposer is a vision-language large model (VLLM) based on InternLM for advanced text-image comprehension and composition. InternLM-XComposer has serveal appealing properties:

Shanghai_AI_Laboratory/internlm-xcomposer-vl-7b 是 ModelScope 魔搭社区上的text-generation模型，采用 apache-2.0 许可，可用 deploy_task text、lmdeploy 0.9.1 部署。

- **Repository**: Shanghai_AI_Laboratory/internlm-xcomposer-vl-7b
- **License**: apache-2.0
- **Tasks**: text-generation
- **Inference backends**: deploy_task text, lmdeploy 0.9.1
- **Downloads**: 1503
- **Stars**: 4
- **Last updated**: 2025-02-26

Source: https://www.modelscope.cn/models/Shanghai_AI_Laboratory/internlm-xcomposer-vl-7b

---

<p align="center">
    <img src="logo.png" width="400"/>
<p>

<p align="center">
    <b><font size="6">InternLM-XComposer</font></b> 
<p>

<div align="center">

[💻Github Repo](https://github.com/InternLM/InternLM-XComposer)

</div>

**InternLM-XComposer** is a vision-language large model (VLLM) based on [InternLM](https://github.com/InternLM/InternLM/tree/main) for advanced text-image comprehension and composition. InternLM-XComposer has serveal appealing properties:

- **Interleaved Text-Image Composition**: InternLM-XComposer can effortlessly generate coherent and contextual articles that seamlessly integrate images, providing a more engaging and immersive reading experience. The interleaved text-image composition is implemented in following steps:

    1. **Text Generation**: It crafts long-form text based on human-provided instructions.
    2. **Image Spoting and Captioning**: It pinpoints optimal locations for image placement and furnishes image descriptions.
    3. **Image Retrieval and Selection**: It select image candidates and identify the image that optimally complements the content.

- **Comprehension with Rich Multilingual Knowledge**: The text-image comprehension is empowered by training on extensive multi-modal multilingual concepts with carefully crafted strategies, resulting in a deep understanding of visual content.
- **Strong performance**: It consistently achieves state-of-the-art results across various benchmarks for vision-language large models, including [MME Benchmark](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models/tree/Evaluation) (English), [MMBench](https://opencompass.org.cn/leaderboard-multimodal) (English), [Seed-Bench](https://huggingface.co/spaces/AILab-CVC/SEED-Bench_Leaderboard) (English), [CCBench](https://opencompass.org.cn/leaderboard-multimodal)(Chinese), and [MMBench-CN](https://opencompass.org.cn/leaderboard-multimodal) (Chineese).

We release InternLM-XComposer series in two versions:

- InternLM-XComposer-VL: The pretrained VLLM model with InternLM as the initialization of the LLM, achieving strong performance on various multimodal benchmarks, e.g., MME Benchmark, MMBench Seed-Bench, CCBench, and MMBench-CN.
- InternLM-XComposer: The finetuned VLLM for *Interleaved Text-Image Composition* and *LLM-based AI assistant*.
  <br>