
Most common position in a pipeline	After a ChatPromptBuilder
Mandatory init variables	"token": The Hugging Face API token. Can be set with `HF_API_TOKEN` or `HF_TOKEN` env var.
Mandatory run variables	“messages”: A list of `ChatMessage` objects representing the chat
Output variables	“replies”: A list of strings with all the replies generated by the LLM
API reference	Generators
GitHub link	https://github.com/deepset-ai/haystack/blob/main/haystack/components/generators/chat/hugging_face_local.py

Overview

Keep in mind that if LLMs run locally, you may need a powerful machine to run them. This depends strongly on the model you select and its parameter count.

📘
This component is designed for chat completion, not for text generation. If you want to use Hugging Face LLMs for text generation, use HuggingFaceLocalGenerator instead.

For remote file authorization, this component uses a HF_API_TOKEN environment variable by default. Otherwise, you can pass a Hugging Face API token at initialization with token:

local_generator = HuggingFaceLocalChatGenerator(token=Secret.from_token("<your-api-key>"))

Streaming

This Generator supports streaming the tokens from the LLM directly in output. To do so, pass a function to the streaming_callback init parameter.

Usage

On its own

from haystack.components.generators.chat import HuggingFaceLocalChatGenerator
from haystack.dataclasses import ChatMessage

generator = HuggingFaceLocalChatGenerator(model="HuggingFaceH4/zephyr-7b-beta")
generator.warm_up()
messages = [ChatMessage.from_user("What's Natural Language Processing? Be brief.")]
print(generator.run(messages))

In a Pipeline

from haystack import Pipeline
from haystack.components.builders.prompt_builder import ChatPromptBuilder
from haystack.components.generators.chat import HuggingFaceLocalChatGenerator
from haystack.dataclasses import ChatMessage
from haystack.utils import Secret

prompt_builder = ChatPromptBuilder()
llm = HuggingFaceLocalChatGenerator(model="HuggingFaceH4/zephyr-7b-beta", token=Secret.from_env_var("HF_API_TOKEN"))

pipe = Pipeline()
pipe.add_component("prompt_builder", prompt_builder)
pipe.add_component("llm", llm)
pipe.connect("prompt_builder.prompt", "llm.messages")
location = "Berlin"
messages = [ChatMessage.from_system("Always respond in German even if some input data is in other languages."),
            ChatMessage.from_user("Tell me about {{location}}")]
pipe.run(data={"prompt_builder": {"template_variables":{"location": location}, "template": messages}})