Using Hugging Face Models with LangChain

Jul 17, 2024

Using Hugging Face Models with LangChain

Overview

  • Objective: Exploring how to use models hosted on Hugging Face both through the Hugging Face Hub and locally in LangChain.

Key Points

  1. Methods of Using Models:

    • Hugging Face Hub (Cloud-based)
    • Local Deployment (On-premise)

  2. Required Installations:

    • LangChain
    • Hugging Face Hub
    • transformers library
    • sentence-transformers for embeddings

Using Hugging Face Hub

  • Common Usage: Pinging the Hugging Face Hub API requires an API token.
  • Advantages: Convenient, externally hosted.
  • Limitations: Not all models supported, performance can vary based on network and model complexity.
  • Example: Using flan-T5-XL
    • Set up a large language model chain with a prompt.
    • Example queries: "What is the capital of France?"
    • Demonstrated effective chain-of-thought prompting.

Using Models Locally

  • Advantages:
    • Fine-tuning Flexibility: Customize models without uploading to Hugging Face.
    • Performance: Utilize personal GPU, adjust settings as needed.
    • Model Compatibility: Some models are only usable locally.
  • Example: Loading flan-T5 locally
    • Using transformers pipeline to simplify tokenization and text generation.
    • Instantiating models like flan-T5 using AutoTokenizer, AutoModelForSeq2SeqLM.
    • Setting up the pipeline and querying it similarly to Hugging Face Hub methodology.
  • Another Example: Working with decoder-only models (e.g., GPT-2)
    • Specific setup needed for causal language modeling.

Challenges with Specific Models

  • Blenderbot:
    • Issue: Hub doesn't support conversational AI models directly.
    • Solution: Load the model locally using the appropriate pipeline.
    • Example Response: "What area is best for growing wine in France?"

Embedding Models Locally

  • Usage: Transform text into vectors using sentence-transformers.
  • Example: Bringing in a model for text embeddings
    • Converting text into a 768-dimension vector representation.
    • Can be used for tasks like semantic search via vector databases like Pinecone or Weaviate.

Conclusion

  • Versatility: Using models locally can overcome some limitations seen with the Hugging Face Hub.
  • Performance Tuning: Local setups allow for GPU selection and optimizations.

Actions:

  • Experiment with both deployment methods to see what fits your needs.
  • For specific models that struggle on the Hub, try local deployments.

Questions & Feedback: Add comments for any queries.

End Notes

  • Subscribe: More tutorials and demos will be shared in the future.