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Pleias-RAG-350M

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PleIAs 339

Hello!

Preface

Nice work here! I'm excited to see models of this size for RAG. I'm going to dive more into your official library next.

Pull Request overview

• Add transformers tag
• Add text-generation pipeline tag

Details

These should make it a tad easier to find this model.

Also, as a heads up, in my experience, a small snippet in the model card with usage is often recommended to get people a good feel of what your model does, e.g.:

rag = RAGWithCitations("PleIAs/Pleias-RAG-350M")

# Define query and sources
query = "What is the capital of France?"
sources = [
    {
        "text": "Paris is the capital and most populous city of France.",
        "metadata": {"source": "Geographic Encyclopedia", "reliability": "high"}
    },
    {
        "text": "The Eiffel Tower is located in Paris, France.",
        "metadata": {"source": "Travel Guide", "year": 2020}
    }
]

# Generate a response
response = rag.generate(query, sources)

# Print the final answer with citations
print(response["processed"]["clean_answer"])

(Except also include the import & printed output)

• Tom Aarsen