Sentence Similarity
Transformers
Safetensors
English
mistral
feature-extraction
text-embedding
embeddings
information-retrieval
beir
text-classification
language-model
text-clustering
text-semantic-similarity
text-evaluation
text-reranking
Sentence Similarity
natural_questions
ms_marco
fever
hotpot_qa
mteb
custom_code
text-embeddings-inference
Instructions to use McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp", trust_remote_code=True) model = AutoModel.from_pretrained("McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +1 -1
config.json
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],
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"attention_dropout": 0.0,
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"auto_map": {
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"AutoModel": "McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-
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},
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"bos_token_id": 1,
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"eos_token_id": 2,
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],
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"attention_dropout": 0.0,
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"auto_map": {
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"AutoModel": "McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp--modeling_mistral_encoder.MistralEncoderModel"
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},
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"bos_token_id": 1,
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"eos_token_id": 2,
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