HuggingFaceLocalGenerator

Generate text using models from Hugging Face that run locally.

Basic Information

• Type: haystack.components.generators.hugging_face_local.HuggingFaceLocalGenerator
• Components it can connect with:
  • PromptBuilder: Receives a prompt from PromptBuilder.
  • AnswerBuilder: Sends generated replies to AnswerBuilder.

Inputs

Parameter	Type	Default	Description
prompt	str		A string representing the prompt.
streaming_callback	Optional[StreamingCallbackT]	None	A callback function that is called when a new token is received from the stream.
generation_kwargs	Optional[Dict[str, Any]]	None	Additional keyword arguments for text generation.

Outputs

Parameter	Type	Default	Description
replies	List[str]		A list of strings representing the generated replies.

Overview

HuggingFaceLocalGenerator generates text using a Hugging Face model that runs locally. Running LLMs locally may need powerful hardware depending on the model and its parameter count.

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

The component supports two task types:

• text-generation: Supported by decoder models like GPT.
• text2text-generation: Supported by encoder-decoder models like T5.

Authorization

For remote files authorization, the component uses a HF_API_TOKEN environment variable by default. Connect Haystack Platform with Hugging Face first. For detailed instructions, see Use Hugging Face Models.

Usage Example

This query pipeline uses HuggingFaceLocalGenerator for local text generation:

components:
  bm25_retriever:
    type: haystack_integrations.components.retrievers.opensearch.bm25_retriever.OpenSearchBM25Retriever
    init_parameters:
      document_store:
        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
        init_parameters:
          hosts:
          index: 'default'
          max_chunk_bytes: 104857600
          embedding_dim: 768
          return_embedding: false
          method:
          mappings:
          settings:
          create_index: true
          http_auth:
          use_ssl:
          verify_certs:
          timeout:
      top_k: 10
      fuzziness: 0

  PromptBuilder:
    type: haystack.components.builders.prompt_builder.PromptBuilder
    init_parameters:
      template: |
        Given the following information, answer the question.

        Context:
        {% for document in documents %}
        {{ document.content }}
        {% endfor %}

        Question: {{ query }}
      required_variables:
      variables:

  HuggingFaceLocalGenerator:
    type: haystack.components.generators.hugging_face_local.HuggingFaceLocalGenerator
    init_parameters:
      model: google/flan-t5-large
      task: text2text-generation
      device:
      token:
        type: env_var
        env_vars:
          - HF_API_TOKEN
          - HF_TOKEN
        strict: false
      generation_kwargs:
        max_new_tokens: 100
        temperature: 0.9
      huggingface_pipeline_kwargs:
      stop_words:
      streaming_callback:

  AnswerBuilder:
    type: haystack.components.builders.answer_builder.AnswerBuilder
    init_parameters:
      pattern:
      reference_pattern:

connections:
  - sender: bm25_retriever.documents
    receiver: PromptBuilder.documents
  - sender: PromptBuilder.prompt
    receiver: HuggingFaceLocalGenerator.prompt
  - sender: HuggingFaceLocalGenerator.replies
    receiver: AnswerBuilder.replies
  - sender: bm25_retriever.documents
    receiver: AnswerBuilder.documents

inputs:
  query:
    - bm25_retriever.query
    - PromptBuilder.query
    - AnswerBuilder.query

outputs:
  answers: AnswerBuilder.answers

max_runs_per_component: 100

metadata: {}

Parameters

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
model	str	google/flan-t5-base	The Hugging Face text generation model name or path.
task	Optional[Literal['text-generation', 'text2text-generation']]	None	The task for the Hugging Face pipeline. Options: text-generation (decoder models like GPT), text2text-generation (encoder-decoder models like T5). If not specified, the component infers the task from the model name.
device	Optional[ComponentDevice]	None	The device for loading the model. If None, automatically selects the default device. If a device or device map is specified in huggingface_pipeline_kwargs, it overrides this parameter.
token	Optional[Secret]	Secret.from_env_var(['HF_API_TOKEN', 'HF_TOKEN'], strict=False)	The token to use as HTTP bearer authorization for remote files. If the token is specified in huggingface_pipeline_kwargs, this parameter is ignored.
generation_kwargs	Optional[Dict[str, Any]]	None	A dictionary with keyword arguments to customize text generation: max_length, max_new_tokens, temperature, top_k, top_p. See Hugging Face documentation.
huggingface_pipeline_kwargs	Optional[Dict[str, Any]]	None	Dictionary with keyword arguments to initialize the Hugging Face pipeline. These override model, task, device, and token init parameters. See Hugging Face documentation.
stop_words	Optional[List[str]]	None	If the model generates a stop word, the generation stops. If you provide this parameter, don't specify stopping_criteria in generation_kwargs.
streaming_callback	Optional[StreamingCallbackT]	None	An optional callable for handling streaming responses.

Run Method Parameters

These are the parameters you can configure for the run() method. You can pass these parameters at query time through the API, in Playground, or when running a job.

Parameter	Type	Default	Description
prompt	str		A string representing the prompt.
streaming_callback	Optional[StreamingCallbackT]	None	A callback function that is called when a new token is received from the stream.
generation_kwargs	Optional[Dict[str, Any]]	None	Additional keyword arguments for text generation.