---
license: other
pipeline_tag: visual-question-answering
---
InternLM-XComposer 2.5 OmniLive
[💻Github Repo](https://github.com/InternLM/InternLM-XComposer)
**InternLM-XComposer2.5-OL**, a specialized generalist multimodal system for streaming video and audio interactions.
InternLM-XComposer2.5-OmniLive 🤗  ï½œ XComposer2.5 OmniLive Technical Report 📄
## 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).
Audio Understanding
```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 = 'Image Understanding
```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)
```
### 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(ä¸æ–‡ï¼‰. For other questions or collaborations, please contact internlm@pjlab.org.cn.