Model reference · open weights

LLM2Vec-Meta-Llama-3-mntp

LLM2Vec-Meta-Llama-3-mntp is an open-weight embedding model from McGill-NLP, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.

Embeddings McGill-NLP 1 variants 221k downloads/mo
Request this model on EU hardware All served models Not on the shared API today — deployed on request.

About

What LLM2Vec-Meta-Llama-3-mntp is

LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders LLM2Vec is a simple recipe to convert decoder-only LLMs into text encoders. It consists of 3 simple steps: 1) enabling bidirectional attention, 2) masked next token prediction, and 3) unsupervised contrastive learning. The model can be further fine-tuned to achieve state-of-the-art performance. - Repository: https://github.com/McGill-NLP/llm2vec - Paper: https://arxiv.org/abs/2404.05961 Installation Usage Questions If you have any question about the code, feel free to email Parishad (parishad.behnamghader@mila.quebec) and Vaibhav (vaibhav.adlakha@mila.quebec).

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

MakerMcGill-NLP
TypeEmbedding models
Context8k tokens
Variants1
Runs withtransformers
Released2024-04-30
Popularity221k downloads / month
Likes22
LicenceOpen weights

How it works

How embedding models work

Your textsentence / documentEncodermaps meaningVectorlist of numbersAn embedding model turns text into a vector, so similar meanings sit close together — the basis of search and RAG.

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

VariantParamsPrecisionVRAMFits 16 GBWeights
LLM2Vec-Meta-Llama-3-8B-Instruct-mntpBF16Weights ↗

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys llm2vec-meta-llama-3-mntp for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (llm2vec-meta-llama-3-mntp below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/embeddings \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"llm2vec-meta-llama-3-mntp","input":"text to embed"}'

Details

Languages, data & research

Languages

en

Tags

transformers safetensors llama feature-extraction text-embedding embeddings information-retrieval beir text-classification language-model text-clustering text-semantic-similarity text-evaluation text-reranking

Papers

Licence

Open weights

Open weights under mit — commercial use is permitted. Deploy it on AxForge EU hardware on request. Read the licence ↗

Sources

Weights & code

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