이 노트북은 Llama-2 LLMLlama2Chat wrapper로 보강하여 Llama-2 chat prompt format을 지원하는 방법을 보여줍니다. LangChain의 여러 LLM 구현을 Llama-2 chat model의 인터페이스로 사용할 수 있습니다. 여기에는 ChatHuggingFace, LlamaCpp, GPT4All 등이 포함됩니다. Llama2ChatBaseChatModel을 구현하는 범용 wrapper이므로 애플리케이션에서 chat model로 사용할 수 있습니다. Llama2Chat은 Message 목록을 필수 chat prompt format으로 변환하고 포맷된 prompt를 str로 래핑된 LLM에 전달합니다.
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferMemory
from langchain_experimental.chat_models import Llama2Chat
아래 chat 애플리케이션 예제에서는 다음 chat prompt_template을 사용합니다:
from langchain.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)

HuggingFaceTextGenInference LLM을 통한 Llama-2 Chat

HuggingFaceTextGenInference LLM은 text-generation-inference 서버에 대한 액세스를 캡슐화합니다. 다음 예제에서 inference 서버는 meta-llama/Llama-2-13b-chat-hf model을 제공합니다. 로컬에서 다음과 같이 시작할 수 있습니다:
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
이는 예를 들어 4개의 RTX 3080ti 카드가 있는 머신에서 작동합니다. 사용 가능한 GPU 수에 맞게 --num_shard 값을 조정하세요. HF_API_TOKEN 환경 변수는 Hugging Face API token을 보유합니다.
# !pip3 install text-generation
로컬 inference 서버에 연결하는 HuggingFaceTextGenInference 인스턴스를 생성하고 이를 Llama2Chat으로 래핑합니다.
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)
그런 다음 LLMChain에서 chat modelprompt_template 및 대화 memory와 함께 사용할 준비가 됩니다.
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.

LlamaCPP LLM을 통한 Llama-2 Chat

LlamaCPP LMM과 함께 Llama-2 chat model을 사용하려면 이 설치 지침을 사용하여 llama-cpp-python 라이브러리를 설치하세요. 다음 예제는 ~/Models/llama-2-7b-chat.Q4_0.gguf에 로컬로 저장된 양자화된 llama-2-7b-chat.Q4_0.gguf model을 사용합니다. LlamaCpp 인스턴스를 생성한 후 llm은 다시 Llama2Chat으로 래핑됩니다
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)
그리고 이전 예제와 동일한 방식으로 사용됩니다.
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!
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
  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
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

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