README.md

---
license: other
pipeline_tag: text-generation
---


<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)

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**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 PIL import image
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()
```

### 通过 Transformers 加载
通过以下的代码加载 InternLM-XComposer2-VL-7B 模型

```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()
# `torch_dtype=torch.float16` 可以令模型以 float16 精度加载，否则 transformers 会将模型加载为 float32，导致显存不足
model = AutoModelForCausalLM.from_pretrained(ckpt_path, torch_dtype=torch.float16, trust_remote_code=True).cuda()
model = model.eval()
```

### 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.