Instructions to use xun/Qwen-Audio-Chat-Int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xun/Qwen-Audio-Chat-Int4 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="xun/Qwen-Audio-Chat-Int4", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("xun/Qwen-Audio-Chat-Int4", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use xun/Qwen-Audio-Chat-Int4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xun/Qwen-Audio-Chat-Int4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xun/Qwen-Audio-Chat-Int4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/xun/Qwen-Audio-Chat-Int4

SGLang

How to use xun/Qwen-Audio-Chat-Int4 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "xun/Qwen-Audio-Chat-Int4" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xun/Qwen-Audio-Chat-Int4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "xun/Qwen-Audio-Chat-Int4" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xun/Qwen-Audio-Chat-Int4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use xun/Qwen-Audio-Chat-Int4 with Docker Model Runner:
```
docker model run hf.co/xun/Qwen-Audio-Chat-Int4
```

Qwen-Audio-Chat-Int4 / README.md

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	---
	language:
	- zh
	- en
	tags:
	- qwen
	- multimodal
	- 音频理解
	license: other
	pipeline_tag: text-generation
	inference: false
	---

	# Qwen-Audio-Chat

	<br>

	<p align="center">
	<img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Audio/audio_logo.jpg" width="400"/>
	<p>
	<br>

	<p align="center">
	Qwen-Audio <a href="https://www.modelscope.cn/models/qwen/QWen-Audio/summary">🤖 <a> \| <a href="https://huggingface.co/Qwen/Qwen-Audio">🤗</a>&nbsp ｜ Qwen-Audio-Chat <a href="https://www.modelscope.cn/models/qwen/QWen-Audio-Chat/summary">🤖 <a>\| <a href="https://huggingface.co/Qwen/Qwen-Audio-Chat">🤗</a>&nbsp
	<br>
	&nbsp&nbsp<a href="https://modelscope.cn/studios/qwen/Qwen-Audio-Chat-Demo/summary">Demo</a>&nbsp \| &nbsp&nbsp<a href="https://qwen-audio.github.io/Qwen-Audio/">Homepage</a>&nbsp ｜ &nbsp<a href="http://arxiv.org/abs/2311.07919">Paper</a>
	</p>
	<br><br>

	Qwen-Audio (Qwen Large Audio Language Model) is the multimodal version of the large model series, Qwen (abbr. Tongyi Qianwen), proposed by Alibaba Cloud. Qwen-Audio accepts diverse audio (human speech, natural sound, music and song) and text as inputs, outputs text. The contribution of Qwen-Audio include:

	- Fundamental audio models: Qwen-Audio is a fundamental multi-task audio-language model that supports various tasks, languages, and audio types, serving as a universal audio understanding model. Building upon Qwen-Audio, we develop Qwen-Audio-Chat through instruction fine-tuning, enabling multi-turn dialogues and supporting diverse audio-oriented scenarios.
	- Multi-task learning framework for all types of audios: To scale up audio-language pre-training, we address the challenge of variation in textual labels associated with different datasets by proposing a multi-task training framework, enabling knowledge sharing and avoiding one-to-many interference. Our model incorporates more than 30 tasks and extensive experiments show the model achieves strong performance.
	- Strong Performance: Experimental results show that Qwen-Audio achieves impressive performance across diverse benchmark tasks without requiring any task-specific fine-tuning, surpassing its counterparts. Specifically, Qwen-Audio achieves state-of-the-art results on the test set of Aishell1, cochlscene, ClothoAQA, and VocalSound.
	- Flexible multi-run chat from audio and text input: Qwen-Audio supports multiple-audio analysis, sound understading and reasoning, music appreciation, and tool usage for speech editing.

	Qwen-Audio 是阿里云研发的大规模音频语言模型（Large Audio Language Model）。Qwen-Audio 可以以多种音频 (包括说话人语音、自然音、音乐、歌声）和文本作为输入，并以文本作为输出。Qwen-Audio 系列模型的特点包括：

	- 音频基石模型：Qwen-Audio是一个性能卓越的通用的音频理解模型，支持各种任务、语言和音频类型。在Qwen-Audio的基础上，我们通过指令微调开发了Qwen-Audio-Chat，支持多轮、多语言、多语言对话。Qwen-Audio和Qwen-Audio-Chat模型均已开源。
	- 兼容多种复杂音频的多任务学习框架：为了避免由于数据收集来源不同以及任务类型不同，带来的音频到文本的一对多的干扰问题，我们提出了一种多任务训练框架，实现相似任务的知识共享，并尽可能减少不同任务之间的干扰。通过提出的框架，Qwen-Audio可以容纳训练超过30多种不同的音频任务；
	- 出色的性能：Qwen-Audio在不需要任何任务特定的微调的情况下，在各种基准任务上取得了领先的结果。具体得，Qwen-Audio在Aishell1、cochlscene、ClothoAQA和VocalSound的测试集上都达到了SOTA；
	- 支持多轮音频和文本对话，支持各种语音场景：Qwen-Audio-Chat支持声音理解和推理、音乐欣赏、多音频分析、多轮音频-文本交错对话以及外部语音工具的使用(如语音编辑)。


	We release Qwen-Audio and Qwen-Audio-Chat, which are pretrained model and Chat model respectively. For more details about Qwen-Audio, please refer to our [Github Repo](https://github.com/QwenLM/Qwen-Audio/tree/main). This repo is the one for Qwen-Audio-Chat.
	<br>

	目前，我们提供了Qwen-Audio和Qwen-Audio-Chat两个模型，分别为预训练模型和Chat模型。如果想了解更多关于信息，请点击[链接](https://github.com/QwenLM/Qwen-Audio/tree/main)查看Github仓库。本仓库为Qwen-Audio-Chat仓库。


	## Requirements
	* python 3.8 and above
	* pytorch 1.12 and above, 2.0 and above are recommended
	* CUDA 11.4 and above are recommended (this is for GPU users)
	* FFmpeg
	<br>

	## Quickstart
	Below, we provide simple examples to show how to use Qwen-Audio with 🤗 Transformers.

	Before running the code, make sure you have setup the environment and installed the required packages. Make sure you meet the above requirements, and then install the dependent libraries.

	```bash
	pip install -r requirements.txt
	```
	Now you can start with Transformers. For more usage, please refer to [tutorial](https://github.com/QwenLM/Qwen-Audio/blob/main/TUTORIAL.md).

	#### 🤖 ModelScope

	To use Qwen-Audio for the inference, all you need to do is to input a few lines of codes as demonstrated below. However, please make sure that you are using the latest code.

	```python
	from modelscope import (
	snapshot_download, AutoModelForCausalLM, AutoTokenizer, GenerationConfig
	)
	import torch
	model_id = 'qwen/Qwen-Audio-Chat'
	revision = 'master'

	model_dir = snapshot_download(model_id, revision=revision)
	torch.manual_seed(1234)

	tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
	if not hasattr(tokenizer, 'model_dir'):
	tokenizer.model_dir = model_dir

	# Note: The default behavior now has injection attack prevention off.
	tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)

	# use bf16
	# model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", trust_remote_code=True, bf16=True).eval()
	# use fp16
	# model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", trust_remote_code=True, fp16=True).eval()
	# use cpu only
	# model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="cpu", trust_remote_code=True).eval()
	# use cuda device
	model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="cuda", trust_remote_code=True).eval()


	# 1st dialogue turn
	query = tokenizer.from_list_format([
	{'audio': 'https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Audio/1272-128104-0000.flac'}, # Either a local path or an url
	{'text': 'what does the person say?'},
	])
	response, history = model.chat(tokenizer, query=query, history=None)
	print(response)
	# The person says: "mister quilter is the apostle of the middle classes and we are glad to welcome his gospel".

	# 2nd dialogue turn
	response, history = model.chat(tokenizer, 'Find the start time and end time of the word "middle classes"', history=history)
	print(response)
	# The word "middle classes" starts at <\|2.33\|> seconds and ends at <\|3.26\|> seconds.
	```


	## License Agreement
	Researchers and developers are free to use the codes and model weights of Qwen-Audio-Chat. We also allow its commercial use. Check our license at [LICENSE](LICENSE) for more details.
	<br>

	## Citation
	If you find our paper and code useful in your research, please consider giving a star and citation

	```BibTeX
	@article{Qwen-Audio,
	title={Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models},
	author={Chu, Yunfei and Xu, Jin and Zhou, Xiaohuan and Yang, Qian and Zhang, Shiliang and Yan, Zhijie and Zhou, Chang and Zhou, Jingren},
	journal={arXiv preprint arXiv:2311.07919},
	year={2023}
	}
	```
	<br>

	## Contact Us

	If you are interested to leave a message to either our research team or product team, feel free to send an email to qianwen_opensource@alibabacloud.com.