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internlm-xcomposer2d5-ol-7b

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---
license: other
pipeline_tag: visual-question-answering
---

<p align="center">
    <img src="assets/logo_en.png" width="650"/>
</p>
<p align="center">
    <b><font size="6">InternLM-XComposer 2.5 OmniLive</font></b>
</p>

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

**InternLM-XComposer2.5-OL**, a specialized generalist multimodal system for streaming video and audio interactions.

<div align="center">
        InternLM-XComposer2.5-OmniLive <a href="https://huggingface.co/internlm/internlm-xcomposer2d5-ol-7b">🤗</a> <a href="https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm-xcomposer2d5-ol-7b"><img src="../assets/modelscope_logo.png" width="20px"></a> &nbsp｜ XComposer2.5 OmniLive Technical Report <a href="https://arxiv.org/abs/2407.03320">  📄 </a>  


## Quickstart

We provide simple examples below to show how to use InternLM-XComposer-2.5-OL with 🤗 Transformers. For complete guide, please refer to [here](examples/README.md).
 

<details>
  <summary>
    <b>Audio Understanding</b>
  </summary>

```python
import os
os.environ['USE_HF'] = 'True'

import torch
from swift.llm import (
    get_model_tokenizer, get_template, ModelType,
    get_default_template_type, inference
)
from swift.utils import seed_everything

model_type = ModelType.qwen2_audio_7b_instruct
model_id_or_path = 'internlm/internlm-xcomposer2d5-ol-7b'
template_type = get_default_template_type(model_type)
print(f'template_type: {template_type}')

model, tokenizer = get_model_tokenizer(model_type, torch.float16, model_id_or_path=model_id_or_path, model_dir='audio',
                                       model_kwargs={'device_map': 'cuda:0'})
model.generation_config.max_new_tokens = 256
template = get_template(template_type, tokenizer)
seed_everything(42)

# Chinese ASR
query = '<audio>Detect the language and recognize the speech.'
response, _ = inference(model, template, query, audios='examples/audios/chinese.mp3')
print(f'query: {query}')
print(f'response: {response}')
```

</details>


<details>
  <summary>
    <b>Image Understanding</b>
  </summary>

```python
import torch
from transformers import AutoModel, AutoTokenizer

torch.set_grad_enabled(False)

# init model and tokenizer
model = AutoModel.from_pretrained('internlm/internlm-xcomposer2d5-ol-7b', model_dir='base', torch_dtype=torch.bfloat16, trust_remote_code=True).cuda().eval().half()
tokenizer = AutoTokenizer.from_pretrained('internlm/internlm-xcomposer2d5-ol-7b', model_dir='base', trust_remote_code=True)
model.tokenizer = tokenizer

query = 'Analyze the given image in a detail manner'
image = ['examples/images/dubai.png']
with torch.autocast(device_type='cuda', dtype=torch.float16):
    response, _ = model.chat(tokenizer, query, image, do_sample=False, num_beams=3, use_meta=True)
print(response)
```

</details>

### 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.