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Hugging Face

This notebook shows how to get started using Hugging Face LLM’s as chat models.

In particular, we will: 1. Utilize the HuggingFaceTextGenInference, HuggingFaceEndpoint, or HuggingFaceHub integrations to instantiate an LLM. 2. Utilize the ChatHuggingFace class to enable any of these LLMs to interface with LangChain’s Chat Messages abstraction. 3. Demonstrate how to use an open-source LLM to power an ChatAgent pipeline

Note: To get started, you’ll need to have a Hugging Face Access Token saved as an environment variable: HUGGINGFACEHUB_API_TOKEN.

%pip install --upgrade --quiet  text-generation transformers google-search-results numexpr langchainhub sentencepiece jinja2
Note: you may need to restart the kernel to use updated packages.

1. Instantiate an LLM

There are three LLM options to choose from.

HuggingFaceTextGenInference

import os

from langchain_community.llms import HuggingFaceTextGenInference

ENDPOINT_URL = "<YOUR_ENDPOINT_URL_HERE>"
HF_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")

llm = HuggingFaceTextGenInference(
inference_server_url=ENDPOINT_URL,
max_new_tokens=512,
top_k=50,
temperature=0.1,
repetition_penalty=1.03,
server_kwargs={
"headers": {
"Authorization": f"Bearer {HF_TOKEN}",
"Content-Type": "application/json",
}
},
)

HuggingFaceEndpoint

from langchain_community.llms import HuggingFaceEndpoint

ENDPOINT_URL = "<YOUR_ENDPOINT_URL_HERE>"
llm = HuggingFaceEndpoint(
endpoint_url=ENDPOINT_URL,
task="text-generation",
model_kwargs={
"max_new_tokens": 512,
"top_k": 50,
"temperature": 0.1,
"repetition_penalty": 1.03,
},
)

HuggingFaceHub

from langchain_community.llms import HuggingFaceHub

llm = HuggingFaceHub(
repo_id="HuggingFaceH4/zephyr-7b-beta",
task="text-generation",
model_kwargs={
"max_new_tokens": 512,
"top_k": 30,
"temperature": 0.1,
"repetition_penalty": 1.03,
},
)
/Users/jacoblee/langchain/langchain/libs/langchain/.venv/lib/python3.10/site-packages/huggingface_hub/utils/_deprecation.py:127: FutureWarning: '__init__' (from 'huggingface_hub.inference_api') is deprecated and will be removed from version '1.0'. `InferenceApi` client is deprecated in favor of the more feature-complete `InferenceClient`. Check out this guide to learn how to convert your script to use it: https://huggingface.co/docs/huggingface_hub/guides/inference#legacy-inferenceapi-client.
warnings.warn(warning_message, FutureWarning)

2. Instantiate the ChatHuggingFace to apply chat templates

Instantiate the chat model and some messages to pass.

from langchain.schema import (
HumanMessage,
SystemMessage,
)
from langchain_community.chat_models.huggingface import ChatHuggingFace

messages = [
SystemMessage(content="You're a helpful assistant"),
HumanMessage(
content="What happens when an unstoppable force meets an immovable object?"
),
]

chat_model = ChatHuggingFace(llm=llm)
WARNING! repo_id is not default parameter.
repo_id was transferred to model_kwargs.
Please confirm that repo_id is what you intended.
WARNING! task is not default parameter.
task was transferred to model_kwargs.
Please confirm that task is what you intended.
WARNING! huggingfacehub_api_token is not default parameter.
huggingfacehub_api_token was transferred to model_kwargs.
Please confirm that huggingfacehub_api_token is what you intended.
None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.

Inspect which model and corresponding chat template is being used.

chat_model.model_id
'HuggingFaceH4/zephyr-7b-beta'

Inspect how the chat messages are formatted for the LLM call.

chat_model._to_chat_prompt(messages)
"<|system|>\nYou're a helpful assistant</s>\n<|user|>\nWhat happens when an unstoppable force meets an immovable object?</s>\n<|assistant|>\n"

Call the model.

res = chat_model.invoke(messages)
print(res.content)
According to a popular philosophical paradox, when an unstoppable force meets an immovable object, it is impossible to determine which one will prevail because both are defined as being completely unyielding and unmovable. The paradox suggests that the very concepts of "unstoppable force" and "immovable object" are inherently contradictory, and therefore, it is illogical to imagine a scenario where they would meet and interact. However, in practical terms, it is highly unlikely for such a scenario to occur in the real world, as the concepts of "unstoppable force" and "immovable object" are often used metaphorically to describe hypothetical situations or abstract concepts, rather than physical objects or forces.

3. Take it for a spin as an agent!

Here we’ll test out Zephyr-7B-beta as a zero-shot ReAct Agent. The example below is taken from here.

Note: To run this section, you’ll need to have a SerpAPI Token saved as an environment variable: SERPAPI_API_KEY

from langchain import hub
from langchain.agents import AgentExecutor, load_tools
from langchain.agents.format_scratchpad import format_log_to_str
from langchain.agents.output_parsers import (
ReActJsonSingleInputOutputParser,
)
from langchain.tools.render import render_text_description
from langchain_community.utilities import SerpAPIWrapper

Configure the agent with a react-json style prompt and access to a search engine and calculator.

# setup tools
tools = load_tools(["serpapi", "llm-math"], llm=llm)

# setup ReAct style prompt
prompt = hub.pull("hwchase17/react-json")
prompt = prompt.partial(
tools=render_text_description(tools),
tool_names=", ".join([t.name for t in tools]),
)

# define the agent
chat_model_with_stop = chat_model.bind(stop=["\nObservation"])
agent = (
{
"input": lambda x: x["input"],
"agent_scratchpad": lambda x: format_log_to_str(x["intermediate_steps"]),
}
| prompt
| chat_model_with_stop
| ReActJsonSingleInputOutputParser()
)

# instantiate AgentExecutor
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
agent_executor.invoke(
{
"input": "Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?"
}
)


> Entering new AgentExecutor chain...
Question: Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?

Thought: I need to use the Search tool to find out who Leo DiCaprio's current girlfriend is. Then, I can use the Calculator tool to raise her current age to the power of 0.43.

Action:
```
{
"action": "Search",
"action_input": "leo dicaprio girlfriend"
}
```
Leonardo DiCaprio may have found The One in Vittoria Ceretti. “They are in love,” a source exclusively reveals in the latest issue of Us Weekly. “Leo was clearly very proud to be showing Vittoria off and letting everyone see how happy they are together.”Now that we know Leo DiCaprio's current girlfriend is Vittoria Ceretti, let's find out her current age.

Action:
```
{
"action": "Search",
"action_input": "vittoria ceretti age"
}
```
25 yearsNow that we know Vittoria Ceretti's current age is 25, let's use the Calculator tool to raise it to the power of 0.43.

Action:
```
{
"action": "Calculator",
"action_input": "25^0.43"
}
```
Answer: 3.991298452658078Final Answer: Vittoria Ceretti, Leo DiCaprio's current girlfriend, when raised to the power of 0.43 is approximately 4.0 rounded to two decimal places. Her current age is 25 years old.

> Finished chain.
{'input': "Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?",
'output': "Vittoria Ceretti, Leo DiCaprio's current girlfriend, when raised to the power of 0.43 is approximately 4.0 rounded to two decimal places. Her current age is 25 years old."}

Wahoo! Our open-source 7b parameter Zephyr model was able to:

  1. Plan out a series of actions: I need to use the Search tool to find out who Leo DiCaprio's current girlfriend is. Then, I can use the Calculator tool to raise her current age to the power of 0.43.
  2. Then execute a search using the SerpAPI tool to find who Leo DiCaprio’s current girlfriend is
  3. Execute another search to find her age
  4. And finally use a calculator tool to calculate her age raised to the power of 0.43

It’s exciting to see how far open-source LLM’s can go as general purpose reasoning agents. Give it a try yourself!