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2 days ago To use HuggingFace models for multimodal retrieval-augmented generation (RAG) in your application, you can leverage the existing support for HuggingFace models in the llama_index.multi_modal_llms module. Here’s a general guide on how to integrate a HuggingFace model like "Qwen/Qwen2-VL-2B-Instruct":
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Install Dependencies: Ensure you have the
huggingface-hubpackage installed:pip install huggingface-hub -
Define Your HuggingFace Model Class: You can create a class similar to
OllamaMultiModalbut for HuggingFace models. Here’s a simplified example:from llama_index.multi_modal_llms.base import MultiModalLLM, MultiModalLLMMetadata from llama_index.llms.huggingface import HuggingFaceLLM from pydantic import Field from typing import Any, Dict, Sequence class HuggingFaceMultiModal(MultiModalLLM): model_name: str = Field(description="The HuggingFace model to use.") temperature: float = Field( default=0.75, description="The temperature to use for sampling.", ge=0.0, le=1.0, ) context_window: int = Field( default=DEFAULT_CONTEXT_WINDOW, description="The maximum number of context tokens for the model.", gt=0, ) additional_kwargs: Dict[str, Any] = Field( default_factory=dict, description="Additional model parameters for the HuggingFace model.", ) def __init__(self, **kwargs: Any) -> None: super().__init__(**kwargs) self.model = HuggingFaceLLM(model_name=self.model_name) @classmethod def class_name(cls) -> str: return "HuggingFace_multi_modal_llm" @property def metadata(self) -> MultiModalLLMMetadata: return MultiModalLLMMetadata( context_window=self.context_window, num_output=DEFAULT_NUM_OUTPUTS, model_name=self.model_name, is_chat_model=True, ) def complete(self, prompt: str, image_documents: Sequence[ImageNode], **kwargs: Any) -> CompletionResponse: # Implement the completion logic using HuggingFace model pass def stream_complete(self, prompt: str, image_documents: Sequence[ImageNode], **kwargs: Any) -> CompletionResponseGen: # Implement the streaming completion logic using HuggingFace model pass def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse: # Implement the chat logic using HuggingFace model pass def stream_chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponseGen: # Implement the streaming chat logic using HuggingFace model pass async def acomplete(self, prompt: str, image_documents: Sequence[ImageNode], **kwargs: Any) -> CompletionResponse: # Implement the async completion logic using HuggingFace model pass async def astream_complete(self, prompt: str, image_documents: Sequence[ImageNode], **kwargs: Any) -> CompletionResponseAsyncGen: # Implement the async streaming completion logic using HuggingFace model pass async def achat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse: # Implement the async chat logic using HuggingFace model pass async def astream_chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponseAsyncGen: # Implement the async streaming chat logic using HuggingFace model pass -
Use the HuggingFace Model: Instantiate and use your
HuggingFaceMultiModalclass in your application:model = HuggingFaceMultiModal( model_name="Qwen/Qwen2-VL-2B-Instruct", temperature=0.7, context_window=2048, additional_kwargs={"some_param": "value"} ) # Example usage response = model.complete(prompt="Your prompt here", image_documents=[]) print(response.text)
This setup allows you to use HuggingFace models in a similar manner to how you used OllamaMultiModal. Make sure to implement the specific logic for completion, streaming completion, chat, and streaming chat methods as per your requirements.
For more details, you can refer to the existing implementations of HuggingFace models in the llama_index.multi_modal_llms module [1].
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Question
I want to use "Qwen/Qwen2-VL-2B-Instruct" on my multimodal rag app. I tried OllamaMultiModal from llama_index.multi_modal_llms.ollama and it works fine. I want to use huggingface models as well