Llama2Chat
本笔记本展示了如何使用Llama2Chat包装器增强Llama-2 LLM，以支持Llama-2 聊天提示格式。LangChain 中的几个LLM实现可以用作 Llama-2 聊天模型的接口。其中包括ChatHuggingFace、LlamaCpp、GPT4All，仅举几个例子。
Llama2Chat 是一个通用包装器，实现了 BaseChatModel，因此可以作为 聊天模型 在应用中使用。Llama2Chat 将消息列表转换为 所需的聊天提示格式，并将格式化后的提示作为 str 传递给包装的 LLM。
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferMemory
from langchain_experimental.chat_models import Llama2Chat
对于下面的聊天应用程序示例，我们将使用以下聊天 prompt_template：
from langchain_core.messages import SystemMessage
from langchain_core.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
MessagesPlaceholder,
)
template_messages = [
SystemMessage(content="You are a helpful assistant."),
MessagesPlaceholder(variable_name="chat_history"),
HumanMessagePromptTemplate.from_template("{text}"),
]
prompt_template = ChatPromptTemplate.from_messages(template_messages)
通过 HuggingFaceTextGenInference LLM与Llama-2聊天
HuggingFaceTextGenInference LLM 封装了对 文本生成推理 服务器的访问。在以下示例中，推理服务器提供了一个 meta-llama/Llama-2-13b-chat-hf 模型。可以通过以下方式在本地启动：
docker run \
--rm \
--gpus all \
--ipc=host \
-p 8080:80 \
-v ~/.cache/huggingface/hub:/data \
-e HF_API_TOKEN=${HF_API_TOKEN} \
ghcr.io/huggingface/text-generation-inference:0.9 \
--hostname 0.0.0.0 \
--model-id meta-llama/Llama-2-13b-chat-hf \
--quantize bitsandbytes \
--num-shard 4
例如，这在一台配备4块RTX 3080ti显卡的机器上可以运行。将--num_shard值调整为可用GPU的数量。HF_API_TOKEN环境变量保存Hugging Face API令牌。
# !pip3 install text-generation
创建一个连接到本地推理服务器的 HuggingFaceTextGenInference 实例，并将其包装为 Llama2Chat。
from langchain_community.llms import HuggingFaceTextGenInference
llm = HuggingFaceTextGenInference(
inference_server_url="http://127.0.0.1:8080/",
max_new_tokens=512,
top_k=50,
temperature=0.1,
repetition_penalty=1.03,
)
model = Llama2Chat(llm=llm)
API 参考：HuggingFaceTextGenInference
然后你就可以在 LLMChain 中结合 model、prompt_template 和对话 memory 使用聊天功能了。
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
chain = LLMChain(llm=model, prompt=prompt_template, memory=memory)
print(
chain.run(
text="What can I see in Vienna? Propose a few locations. Names only, no details."
)
)
Sure, I'd be happy to help! Here are a few popular locations to consider visiting in Vienna:
1. Schönbrunn Palace
2. St. Stephen's Cathedral
3. Hofburg Palace
4. Belvedere Palace
5. Prater Park
6. Vienna State Opera
7. Albertina Museum
8. Museum of Natural History
9. Kunsthistorisches Museum
10. Ringstrasse
print(chain.run(text="Tell me more about #2."))
Certainly! St. Stephen's Cathedral (Stephansdom) is one of the most recognizable landmarks in Vienna and a must-see attraction for visitors. This stunning Gothic cathedral is located in the heart of the city and is known for its intricate stone carvings, colorful stained glass windows, and impressive dome.
The cathedral was built in the 12th century and has been the site of many important events throughout history, including the coronation of Holy Roman emperors and the funeral of Mozart. Today, it is still an active place of worship and offers guided tours, concerts, and special events. Visitors can climb up the south tower for panoramic views of the city or attend a service to experience the beautiful music and chanting.
通过 LlamaCPP LLM与Llama-2聊天
要使用带有 LlamaCPP 的 Llama-2 聊天模型 LMM，请按照 这些安装说明 安装 llama-cpp-python 库。以下示例使用存储在本地 ~/Models/llama-2-7b-chat.Q4_0.gguf 的量化模型 llama-2-7b-chat.Q4_0.gguf。
在创建 LlamaCpp 实例后，llm 再次被包装进 Llama2Chat
from os.path import expanduser
from langchain_community.llms import LlamaCpp
model_path = expanduser("~/Models/llama-2-7b-chat.Q4_0.gguf")
llm = LlamaCpp(
model_path=model_path,
streaming=False,
)
model = Llama2Chat(llm=llm)
API 参考：LlamaCpp
并且使用方式与前面的例子相同。
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
chain = LLMChain(llm=model, prompt=prompt_template, memory=memory)
print(
chain.run(
text="What can I see in Vienna? Propose a few locations. Names only, no details."
)
)
Of course! Vienna is a beautiful city with a rich history and culture. Here are some of the top tourist attractions you might want to consider visiting:
1. Schönbrunn Palace
2. St. Stephen's Cathedral
3. Hofburg Palace
4. Belvedere Palace
5. Prater Park
6. MuseumsQuartier
7. Ringstrasse
8. Vienna State Opera
9. Kunsthistorisches Museum
10. Imperial Palace
These are just a few of the many amazing places to see in Vienna. Each one has its own unique history and charm, so I hope you enjoy exploring this beautiful city!
``````output
llama_print_timings: load time = 250.46 ms
llama_print_timings: sample time = 56.40 ms / 144 runs ( 0.39 ms per token, 2553.37 tokens per second)
llama_print_timings: prompt eval time = 1444.25 ms / 47 tokens ( 30.73 ms per token, 32.54 tokens per second)
llama_print_timings: eval time = 8832.02 ms / 143 runs ( 61.76 ms per token, 16.19 tokens per second)
llama_print_timings: total time = 10645.94 ms
print(chain.run(text="Tell me more about #2."))
Llama.generate: prefix-match hit
``````output
Of course! St. Stephen's Cathedral (also known as Stephansdom) is a stunning Gothic-style cathedral located in the heart of Vienna, Austria. It is one of the most recognizable landmarks in the city and is considered a symbol of Vienna.
Here are some interesting facts about St. Stephen's Cathedral:
1. History: The construction of St. Stephen's Cathedral began in the 12th century on the site of a former Romanesque church, and it took over 600 years to complete. The cathedral has been renovated and expanded several times throughout its history, with the most significant renovation taking place in the 19th century.
2. Architecture: St. Stephen's Cathedral is built in the Gothic style, characterized by its tall spires, pointed arches, and intricate stone carvings. The cathedral features a mix of Romanesque, Gothic, and Baroque elements, making it a unique blend of styles.
3. Design: The cathedral's design is based on the plan of a cross with a long nave and two shorter arms extending from it. The main altar is
``````output
llama_print_timings: load time = 250.46 ms
llama_print_timings: sample time = 100.60 ms / 256 runs ( 0.39 ms per token, 2544.73 tokens per second)
llama_print_timings: prompt eval time = 5128.71 ms / 160 tokens ( 32.05 ms per token, 31.20 tokens per second)
llama_print_timings: eval time = 16193.02 ms / 255 runs ( 63.50 ms per token, 15.75 tokens per second)
llama_print_timings: total time = 21988.57 ms