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McGill-NLP
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LLM2Vec-Mistral-7B-Instruct-v2-mntp-supervised

Sentence Similarity
PEFT
Safetensors
English
text-embedding
embeddings
information-retrieval
beir
text-classification
language-model
text-clustering
text-semantic-similarity
text-evaluation
text-reranking
feature-extraction
Sentence Similarity
natural_questions
ms_marco
fever
hotpot_qa
mteb
Eval Results
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vaibhavad commited on
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Update README.md

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@@ -45,7 +45,7 @@ from transformers import AutoTokenizer, AutoModel, AutoConfig
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  from peft import PeftModel
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  # Loading base Mistral model, along with custom code that enables bidirectional connections in decoder-only LLMs. MNTP LoRA weights are merged into the base model.
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- .tokenizer = AutoTokenizer.from_pretrained("McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp")
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  config = AutoConfig.from_pretrained("McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp", trust_remote_code=True)
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  model = AutoModel.from_pretrained("McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp", trust_remote_code=True, config=config, torch_dtype=torch.bfloat16)
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  model = PeftModel.from_pretrained(model, "McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp")
 
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  from peft import PeftModel
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  # Loading base Mistral model, along with custom code that enables bidirectional connections in decoder-only LLMs. MNTP LoRA weights are merged into the base model.
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+ tokenizer = AutoTokenizer.from_pretrained("McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp")
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  config = AutoConfig.from_pretrained("McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp", trust_remote_code=True)
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  model = AutoModel.from_pretrained("McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp", trust_remote_code=True, config=config, torch_dtype=torch.bfloat16)
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  model = PeftModel.from_pretrained(model, "McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp")