McGill-NLP/llm2vec

Health v2 · maintenance only

Adoption not part of the score

1712 stars · 139 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

LLM2Vec is a Python library that converts decoder-only large language models into powerful text encoders via bidirectional attention, masked next token prediction, and unsupervised contrastive learning. It wraps HuggingFace models for sequence encoding and pooling, and ships pretrained checkpoints on HuggingFace.

Use cases

• convert an LLM into a text embedding model
• generate sentence embeddings for semantic search
• build embeddings for RAG retrieval
• fine-tune a decoder-only LLM as a text encoder
• evaluate embeddings on MTEB benchmarks

When to choose

• you want state-of-the-art text embeddings derived from open LLMs
• you need custom embedding models fine-tuned on your domain
• you want bidirectional encoders without training from scratch

When to avoid

• you need a lightweight embedding model for CPU-only deployment
• you only need off-the-shelf embeddings without GPU training
• your stack does not support HuggingFace transformers and flash-attention

Facets

library · maturity active

machine-learning llm-training rag nlp large-language-models machine-learning python text-embeddings sentence-embeddings contrastive-learning huggingface research natural-language-processing retrieval-augmented-generation gpu

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markdown · JSON · MCP: product_card(name="McGill-NLP/llm2vec")