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  library_name: transformers
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- tags: []
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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-
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- ### Model Description
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-
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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-
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- ## Uses
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-
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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-
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- [More Information Needed]
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- ### Results
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- [More Information Needed]
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- **APA:**
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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- [More Information Needed]
 
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  ---
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  library_name: transformers
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+ license: mit
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+ language:
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+ - en
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+ pipeline_tag: sentence-similarity
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+ tags:
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+ - text-embedding
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+ - embeddings
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+ - information-retrieval
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+ - beir
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+ - text-classification
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+ - language-model
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+ - text-clustering
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+ - text-semantic-similarity
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+ - text-evaluation
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+ - text-reranking
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+ - feature-extraction
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+ - sentence-similarity
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+ - Sentence Similarity
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+ - natural_questions
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+ - ms_marco
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+ - fever
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+ - hotpot_qa
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+ - mteb
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  ---
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+ # LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
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+
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+ > 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.
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+ - **Repository:** https://github.com/McGill-NLP/llm2vec
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+ - **Paper:** https://arxiv.org/abs/2404.05961
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+
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+ ## Installation
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+ ```bash
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+ pip install llm2vec
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+ ```
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+
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+ ## Usage
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+ ```python
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+ from llm2vec import LLM2Vec
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+
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+ import torch
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+ from transformers import AutoTokenizer, AutoModel, AutoConfig
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+ from peft import PeftModel
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+
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+ # Loading base Mistral model, along with custom code that enables bidirectional connections in decoder-only LLMs.
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+ tokenizer = AutoTokenizer.from_pretrained(
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+ "McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp"
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+ )
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+ config = AutoConfig.from_pretrained(
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+ "McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp", trust_remote_code=True
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+ )
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+ model = AutoModel.from_pretrained(
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+ "McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp",
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+ trust_remote_code=True,
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+ config=config,
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+ torch_dtype=torch.bfloat16,
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+ device_map="cuda" if torch.cuda.is_available() else "cpu",
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+ )
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+
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+ # Loading MNTP (Masked Next Token Prediction) model.
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+ model = PeftModel.from_pretrained(
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+ model,
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+ "McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp",
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+ )
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+
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+ # Wrapper for encoding and pooling operations
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+ l2v = LLM2Vec(model, tokenizer, pooling_mode="mean", max_length=512)
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+
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+ # Encoding queries using instructions
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+ instruction = (
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+ "Given a web search query, retrieve relevant passages that answer the query:"
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+ )
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+ queries = [
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+ [instruction, "how much protein should a female eat"],
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+ [instruction, "summit define"],
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+ ]
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+ q_reps = l2v.encode(queries)
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+
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+ # Encoding documents. Instruction are not required for documents
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+ documents = [
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+ "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
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+ "Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.",
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+ ]
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+ d_reps = l2v.encode(documents)
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+
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+ # Compute cosine similarity
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+ q_reps_norm = torch.nn.functional.normalize(q_reps, p=2, dim=1)
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+ d_reps_norm = torch.nn.functional.normalize(d_reps, p=2, dim=1)
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+ cos_sim = torch.mm(q_reps_norm, d_reps_norm.transpose(0, 1))
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+
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+ print(cos_sim)
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+ """
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+ tensor([[0.7740, 0.5580],
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+ [0.4845, 0.4993]])
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+ """
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+ ```
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+
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+ ## Questions
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+ If you have any question about the code, feel free to email Parishad (`parishad.behnamghader@mila.quebec`) and Vaibhav (`vaibhav.adlakha@mila.quebec`).