---
title: internlm-xcomposer2-vl-7b
canonical_url: "https://www.modelscope.cn/models/Shanghai_AI_Laboratory/internlm-xcomposer2-vl-7b"
md_url: "https://www.modelscope.cn/models/Shanghai_AI_Laboratory/internlm-xcomposer2-vl-7b.md"
repository: Shanghai_AI_Laboratory/internlm-xcomposer2-vl-7b
chinese_name: "浦语·灵笔2-视觉问答-7B"
last_updated: 2025-02-26
license: other
pipeline_tag: visual-question-answering
tasks:
  - visual-question-answering
model_type:
  - internlmxcomposer2
architectures:
  - InternLMXComposer2ForCausalLM
library_name:
  - pytorch
  - transformer
frameworks:
  - Pytorch
inference_backends:
  - "deploy_task vlm"
  - "lmdeploy_turbomind 0.9.1"
downloads: 15136
stars: 16
---

# internlm-xcomposer2-vl-7b

> internlm-xcomposer2-vl-7b - Shanghai_AI_Laboratory 在 ModelScope 开源的模型。InternLM-XComposer2 is a vision-language large model (VLLM) based on InternLM2 for advanced text-image comprehension and composition.

Shanghai_AI_Laboratory/internlm-xcomposer2-vl-7b 是 ModelScope 魔搭社区上的visual-question-answering模型，采用 other 许可，可用 deploy_task vlm、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: Shanghai_AI_Laboratory/internlm-xcomposer2-vl-7b
- **License**: other
- **Tasks**: visual-question-answering
- **Inference backends**: deploy_task vlm, lmdeploy_turbomind 0.9.1
- **Downloads**: 15136
- **Stars**: 16
- **Last updated**: 2025-02-26

Source: https://www.modelscope.cn/models/Shanghai_AI_Laboratory/internlm-xcomposer2-vl-7b

---

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

<p align="center">
    <b><font size="6">InternLM-XComposer2</font></b> 
<p>

<div align="center">

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

[Paper](https://arxiv.org/abs/2401.16420)

</div>

**InternLM-XComposer2** is a vision-language large model (VLLM) based on [InternLM2](https://github.com/InternLM/InternLM) for advanced text-image comprehension and composition. 

We release InternLM-XComposer2 series in two versions:

- InternLM-XComposer2-VL: The pretrained VLLM model with InternLM2 as the initialization of the LLM, achieving strong performance on various multimodal benchmarks.
- InternLM-XComposer2: The finetuned VLLM for *Free-from Interleaved Text-Image Composition*.


### Import from Transformers
To load the InternLM-XComposer2-VL-7B model using Transformers, use the following code:
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
ckpt_path = "internlm/internlm-xcomposer2-vl-7b"
tokenizer = AutoTokenizer.from_pretrained(ckpt_path, trust_remote_code=True).cuda()
# Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
model = AutoModelForCausalLM.from_pretrained(ckpt_path, torch_dtype=torch.float16, trust_remote_code=True).cuda()
model = model.eval()
```

## Quickstart
We provide a simple example to show how to use InternLM-XComposer with 🤗 Transformers.
```python
import torch
from transformers import AutoModel, AutoTokenizer

torch.set_grad_enabled(False)

# init model and tokenizer
model = AutoModel.from_pretrained('internlm/internlm-xcomposer2-vl-7b', trust_remote_code=True).cuda().eval()
tokenizer = AutoTokenizer.from_pretrained('internlm/internlm-xcomposer2-vl-7b', trust_remote_code=True)

query = '<ImageHere>Please describe this image in detail.'
image = './image1.webp'
with torch.cuda.amp.autocast():
  response, _ = model.chat(tokenizer, query=query, image=image, history=[], do_sample=False)
print(response)
#The image features a quote by Oscar Wilde, "Live life with no excuses, travel with no regret,"
# set against a backdrop of a breathtaking sunset. The sky is painted in hues of pink and orange,
# creating a serene atmosphere. Two silhouetted figures stand on a cliff, overlooking the horizon.
# They appear to be hiking or exploring, embodying the essence of the quote.
# The overall scene conveys a sense of adventure and freedom, encouraging viewers to embrace life without hesitation or regrets.

```

### Open Source License
The code is licensed under Apache-2.0, while model weights are fully open for academic research and also allow free commercial usage. To apply for a commercial license, please fill in the application form (English)/申请表（中文）. For other questions or collaborations, please contact internlm@pjlab.org.cn.