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## 什么是HuggingFace Agent
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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：
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- run模式：单轮对话，没有上下文，单个prompt多tool组合调用能力好
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- 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
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> 详见官方文档：[Transformers Agents](https://huggingface.co/docs/transformers/transformers_agents)
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## 使用通义千问作为Agent
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### 安装依赖
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```
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pip install transformers
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```
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### 构建QWenAgent
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以下代码便可实现QWenAgent：
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, Agent
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from transformers.generation import GenerationConfig
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class QWenAgent(Agent):
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"""
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Agent that uses QWen model and tokenizer to generate code.
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Args:
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chat_prompt_template (`str`, *optional*):
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Pass along your own prompt if you want to override the default template for the `chat` method. Can be the
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actual prompt template or a repo ID (on the Hugging Face Hub). The prompt should be in a file named
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`chat_prompt_template.txt` in this repo in this case.
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run_prompt_template (`str`, *optional*):
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Pass along your own prompt if you want to override the default template for the `run` method. Can be the
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actual prompt template or a repo ID (on the Hugging Face Hub). The prompt should be in a file named
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`run_prompt_template.txt` in this repo in this case.
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additional_tools ([`Tool`], list of tools or dictionary with tool values, *optional*):
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Any additional tools to include on top of the default ones. If you pass along a tool with the same name as
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one of the default tools, that default tool will be overridden.
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Example:
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```py
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agent = QWenAgent()
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agent.run("Draw me a picture of rivers and lakes.")
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```
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"""
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def __init__(self, chat_prompt_template=None, run_prompt_template=None, additional_tools=None):
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checkpoint = "Qwen/Qwen-7B-Chat"
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self.tokenizer = AutoTokenizer.from_pretrained(checkpoint, trust_remote_code=True)
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self.model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", trust_remote_code=True).cuda().eval()
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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
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self.model.generation_config.do_sample = False # greedy
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super().__init__(
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chat_prompt_template=chat_prompt_template,
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run_prompt_template=run_prompt_template,
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additional_tools=additional_tools,
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)
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def generate_one(self, prompt, stop):
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# "Human:" 和 "Assistant:" 曾为通义千问的特殊保留字，需要替换为 "_HUMAN_:" 和 "_ASSISTANT_:"。这一问题将在未来版本修复。
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prompt = prompt.replace("Human:", "_HUMAN_:").replace("Assistant:", "_ASSISTANT_:")
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stop = [item.replace("Human:", "_HUMAN_:").replace("Assistant:", "_ASSISTANT_:") for item in stop]
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result, _ = self.model.chat(self.tokenizer, prompt, history=None)
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for stop_seq in stop:
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if result.endswith(stop_seq):
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result = result[: -len(stop_seq)]
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result = result.replace("_HUMAN_:", "Human:").replace("_ASSISTANT_:", "Assistant:")
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return result
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agent = QWenAgent()
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agent.run("Draw me a picture of rivers and lakes.")
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```
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### 使用示例
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```python
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agent = QWenAgent()
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agent.run("generate an image of panda", remote=True)
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```
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> 更多玩法参考HuggingFace官方文档[Transformers Agents](https://huggingface.co/docs/transformers/transformers_agents)
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## Tools
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### Tools支持
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HuggingFace Agent官方14个tool：
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- **Document question answering**: given a document (such as a PDF) in image format, answer a question on this document (Donut)
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- **Text question answering**: given a long text and a question, answer the question in the text (Flan-T5)
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- **Unconditional image captioning**: Caption the image! (BLIP)
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- **Image question answering**: given an image, answer a question on this image (VILT)
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- **Image segmentation**: given an image and a prompt, output the segmentation mask of that prompt (CLIPSeg)
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- **Speech to text**: given an audio recording of a person talking, transcribe the speech into text (Whisper)
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- **Text to speech**: convert text to speech (SpeechT5)
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- **Zero-shot text classification**: given a text and a list of labels, identify to which label the text corresponds the most (BART)
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- **Text summarization**: summarize a long text in one or a few sentences (BART)
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- **Translation**: translate the text into a given language (NLLB)
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- **Text downloader**: to download a text from a web URL
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- **Text to image**: generate an image according to a prompt, leveraging stable diffusion
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- **Image transformation**: transforms an image
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- **Text to video**: generate a small video according to a prompt, leveraging damo-vilab
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### Tools模型部署
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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：
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> agent.run(xxx, remote=True)
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HuggingFace没有在线部署的模型会自动下载checkpoint进行本地inference
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网络原因偶尔连不上HuggingFace，请多次尝试
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