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Transformers_IPFS

Load models directly from IPFS for Hugging Face Transformers.

Features

  • 🌐 Direct integration with local IPFS nodes (preferred method)
  • 🔄 Automatic fallback to IPFS gateways when local node isn't available
  • 🔍 Simple URI format: ipfs://CID for easy model sharing
  • ⚡ Zero configuration required - works automatically once installed
  • 🧩 Compatible with any version of Transformers

Installation

pip install transformers-ipfs
transformers-ipfs activate

Once installed and activated, the transformers_ipfs integration will be loaded automatically whenever you use Python.

Usage

Use the Transformers library with IPFS model URIs:

from transformers import AutoModel, AutoTokenizer

# Load a model directly from IPFS
tokenizer = AutoTokenizer.from_pretrained("ipfs://bafybeichqdarufyutqc7yd43k77fkxbmeuhhetbihd3g32ghcqvijp6fxi")
# Equivalent HuggingFace model: "riturajpandey739/gpt2-sentiment-analysis-tweets"
model = AutoModel.from_pretrained("ipfs://bafybeichqdarufyutqc7yd43k77fkxbmeuhhetbihd3g32ghcqvijp6fxi")

IPFS Node Connectivity

The transformers_ipfs package prioritizes connectivity in the following order:

  1. Local IPFS Node (Recommended): If you have an IPFS daemon running locally (ipfs daemon), the package will automatically detect and use it. This method:

    • Is much faster for repeated downloads
    • More reliably loads complex model directories with multiple files
    • Contributes to the IPFS network by providing content to others
  2. IPFS Gateway (Fallback): If a local node isn't available, the package will fall back to public gateways. This method:

    • Works without installing IPFS
    • May be less reliable for complex model directories
    • Downloads can be interrupted more easily

Command Line Interface

# Activate the auto-loading
transformers-ipfs activate

# Check if the integration is active
transformers-ipfs status

# Test the integration
transformers-ipfs test

# Deactivate the integration
transformers-ipfs deactivate

Dependencies

  • Python 3.7+
  • transformers

License

MIT

Metadata

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