mirror of
https://github.com/QwenLM/Qwen.git
synced 2026-05-21 00:45:48 +08:00
102 lines
4.9 KiB
Markdown
102 lines
4.9 KiB
Markdown
## 什么是HuggingFace Agent
|
||
使用大模型作为Agent,仅需自然语言就可调用HuggingFace中的模型,目前支持两种模式:
|
||
|
||
- run模式:单轮对话,没有上下文,单个prompt多tool组合调用能力好
|
||
- chat模式:多轮对话,有上下文,单次调用能力好,可能需要多次prompt实现多tool组合调用
|
||
> 详见官方文档:[Transformers Agents](https://huggingface.co/docs/transformers/transformers_agents)
|
||
|
||
## 使用通义千问作为Agent
|
||
### 安装依赖
|
||
```
|
||
pip install transformers
|
||
```
|
||
### 构建QWenAgent
|
||
以下代码便可实现QWenAgent:
|
||
```python
|
||
import torch
|
||
from transformers import AutoModelForCausalLM, AutoTokenizer, Agent
|
||
from transformers.generation import GenerationConfig
|
||
|
||
|
||
class QWenAgent(Agent):
|
||
"""
|
||
Agent that uses QWen model and tokenizer to generate code.
|
||
|
||
Args:
|
||
chat_prompt_template (`str`, *optional*):
|
||
Pass along your own prompt if you want to override the default template for the `chat` method. Can be the
|
||
actual prompt template or a repo ID (on the Hugging Face Hub). The prompt should be in a file named
|
||
`chat_prompt_template.txt` in this repo in this case.
|
||
run_prompt_template (`str`, *optional*):
|
||
Pass along your own prompt if you want to override the default template for the `run` method. Can be the
|
||
actual prompt template or a repo ID (on the Hugging Face Hub). The prompt should be in a file named
|
||
`run_prompt_template.txt` in this repo in this case.
|
||
additional_tools ([`Tool`], list of tools or dictionary with tool values, *optional*):
|
||
Any additional tools to include on top of the default ones. If you pass along a tool with the same name as
|
||
one of the default tools, that default tool will be overridden.
|
||
|
||
Example:
|
||
|
||
```py
|
||
agent = QWenAgent()
|
||
agent.run("Draw me a picture of rivers and lakes.")
|
||
```
|
||
"""
|
||
def __init__(self, chat_prompt_template=None, run_prompt_template=None, additional_tools=None):
|
||
checkpoint = "Qwen/Qwen-7B-Chat"
|
||
self.tokenizer = AutoTokenizer.from_pretrained(checkpoint, trust_remote_code=True)
|
||
self.model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", trust_remote_code=True).cuda().eval()
|
||
self.model.generation_config = GenerationConfig.from_pretrained(checkpoint, trust_remote_code=True) # 可指定不同的生成长度、top_p等相关超参
|
||
model.generation_config.do_sample = False # greedy
|
||
|
||
super().__init__(
|
||
chat_prompt_template=chat_prompt_template,
|
||
run_prompt_template=run_prompt_template,
|
||
additional_tools=additional_tools,
|
||
)
|
||
|
||
def generate_one(self, prompt, stop):
|
||
result, _ = self.model.chat(self.tokenizer, prompt, history=None)
|
||
for stop_seq in stop:
|
||
if result.endswith(stop_seq):
|
||
result = result[: -len(stop_seq)]
|
||
return result
|
||
|
||
|
||
agent = QWenAgent()
|
||
agent.run("Draw me a picture of rivers and lakes.")
|
||
```
|
||
### 使用示例
|
||
```python
|
||
agent = QWenAgent()
|
||
agent.run("generate an image of panda", remote=True)
|
||
```
|
||

|
||

|
||

|
||
> 更多玩法参考HuggingFace官方文档[Transformers Agents](https://huggingface.co/docs/transformers/transformers_agents)
|
||
|
||
## Tools
|
||
### Tools支持
|
||
HuggingFace Agent官方14个tool:
|
||
|
||
- **Document question answering**: given a document (such as a PDF) in image format, answer a question on this document (Donut)
|
||
- **Text question answering**: given a long text and a question, answer the question in the text (Flan-T5)
|
||
- **Unconditional image captioning**: Caption the image! (BLIP)
|
||
- **Image question answering**: given an image, answer a question on this image (VILT)
|
||
- **Image segmentation**: given an image and a prompt, output the segmentation mask of that prompt (CLIPSeg)
|
||
- **Speech to text**: given an audio recording of a person talking, transcribe the speech into text (Whisper)
|
||
- **Text to speech**: convert text to speech (SpeechT5)
|
||
- **Zero-shot text classification**: given a text and a list of labels, identify to which label the text corresponds the most (BART)
|
||
- **Text summarization**: summarize a long text in one or a few sentences (BART)
|
||
- **Translation**: translate the text into a given language (NLLB)
|
||
- **Text downloader**: to download a text from a web URL
|
||
- **Text to image**: generate an image according to a prompt, leveraging stable diffusion
|
||
- **Image transformation**: transforms an image
|
||
- **Text to video**: generate a small video according to a prompt, leveraging damo-vilab
|
||
### Tools模型部署
|
||
部分工具涉及的模型HuggingFace已进行在线部署,仅需设置remote=True便可实现在线调用:
|
||
> agent.run(xxx, remote=True)
|
||
|
||
HuggingFace没有在线部署的模型会自动下载checkpoint进行本地inference
|
||
网络原因偶尔连不上HuggingFace,请多次尝试 |