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Showing content from https://python.langchain.com/docs/integrations/chat/huggingface/ below:

ChatHuggingFace | 🦜️🔗 LangChain

This will help you get started with langchain_huggingface chat models. For detailed documentation of all ChatHuggingFace features and configurations head to the API reference. For a list of models supported by Hugging Face check out this page.

To access Hugging Face models you'll need to create a Hugging Face account, get an API key, and install the langchain-huggingface integration package.

Generate a Hugging Face Access Token and store it as an environment variable: HUGGINGFACEHUB_API_TOKEN.

import getpass
import os

if not os.getenv("HUGGINGFACEHUB_API_TOKEN"):
    os.environ["HUGGINGFACEHUB_API_TOKEN"] = getpass.getpass("Enter your token: ")

To access langchain_huggingface models you'll need to create a Hugging Face account, get an API key, and install the langchain-huggingface integration package.

You'll need to have a Hugging Face Access Token saved as an environment variable: HUGGINGFACEHUB_API_TOKEN.

import getpass
import os

os.environ["HUGGINGFACEHUB_API_TOKEN"] = getpass.getpass(
    "Enter your Hugging Face API key: "
)

%pip install --upgrade --quiet  langchain-huggingface text-generation transformers google-search-results numexpr langchainhub sentencepiece jinja2 bitsandbytes accelerate

[notice] A new release of pip is available: 24.0 -> 24.1.2
[notice] To update, run: pip install --upgrade pip
Note: you may need to restart the kernel to use updated packages.

You can instantiate a ChatHuggingFace model in two different ways, either from a HuggingFaceEndpoint or from a HuggingFacePipeline.

from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint

llm = HuggingFaceEndpoint(
    repo_id="deepseek-ai/DeepSeek-R1-0528",
    task="text-generation",
    max_new_tokens=512,
    do_sample=False,
    repetition_penalty=1.03,
    provider="auto",  
)

chat_model = ChatHuggingFace(llm=llm)

The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.
Token is valid (permission: fineGrained).
Your token has been saved to /Users/isaachershenson/.cache/huggingface/token
Login successful

Now let's take advantage of Inference Providers to run the model on specific third-party providers

llm = HuggingFaceEndpoint(
    repo_id="deepseek-ai/DeepSeek-R1-0528",
    task="text-generation",
    provider="hyperbolic",  
    
    
)

chat_model = ChatHuggingFace(llm=llm)

from langchain_huggingface import ChatHuggingFace, HuggingFacePipeline

llm = HuggingFacePipeline.from_model_id(
    model_id="HuggingFaceH4/zephyr-7b-beta",
    task="text-generation",
    pipeline_kwargs=dict(
        max_new_tokens=512,
        do_sample=False,
        repetition_penalty=1.03,
    ),
)

chat_model = ChatHuggingFace(llm=llm)

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To run a quantized version of your model, you can specify a bitsandbytes quantization config as follows:

from transformers import BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="float16",
    bnb_4bit_use_double_quant=True,
)

and pass it to the HuggingFacePipeline as a part of its model_kwargs:

llm = HuggingFacePipeline.from_model_id(
    model_id="HuggingFaceH4/zephyr-7b-beta",
    task="text-generation",
    pipeline_kwargs=dict(
        max_new_tokens=512,
        do_sample=False,
        repetition_penalty=1.03,
        return_full_text=False,
    ),
    model_kwargs={"quantization_config": quantization_config},
)

chat_model = ChatHuggingFace(llm=llm)

from langchain_core.messages import (
    HumanMessage,
    SystemMessage,
)

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

ai_msg = chat_model.invoke(messages)

According to the popular phrase and hypothetical scenario, when an unstoppable force meets an immovable object, a paradoxical situation arises as both forces are seemingly contradictory. On one hand, an unstoppable force is an entity that cannot be stopped or prevented from moving forward, while on the other hand, an immovable object is something that cannot be moved or displaced from its position. 

In this scenario, it is un

For detailed documentation of all ChatHuggingFace features and configurations head to the API reference: https://python.langchain.com/api_reference/huggingface/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html

For detailed documentation of all ChatHuggingFace features and configurations head to the API reference: https://python.langchain.com/api_reference/huggingface/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html

• Chat model conceptual guide
• Chat model how-to guides

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