Update README.md

README.md

@@ -1,201 +1,88 @@
 ---
 library_name: transformers
-tags: []
+license: mit
+language:
+- en
+pipeline_tag: sentence-similarity
+tags:
+- text-embedding
+- embeddings
+- information-retrieval
+- beir
+- text-classification
+- language-model
+- text-clustering
+- text-semantic-similarity
+- text-evaluation
+- text-reranking
+- feature-extraction
+- sentence-similarity
+- Sentence Similarity
+- natural_questions
+- ms_marco
+- fever
+- hotpot_qa
+- mteb
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
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-### Model Sources [optional]
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-## Uses
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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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-### Downstream Use [optional]
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-### Out-of-Scope Use
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-## Bias, Risks, and Limitations
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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.
-
-## 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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-## 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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-### Training Procedure 
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-#### Speeds, Sizes, Times [optional]
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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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-#### Factors
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-#### Metrics
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-### Results
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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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-## 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 -->
-
-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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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-## Citation [optional]
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-## Glossary [optional]
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-
+# 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
+
+
+## Installation
+```bash
+pip install llm2vec
+```
+
+## Usage
+```python
+from llm2vec import LLM2Vec
+
+import torch
+from transformers import AutoTokenizer, AutoModel, AutoConfig
+from peft import PeftModel
+
+# 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.
+.tokenizer = AutoTokenizer.from_pretrained("McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp")
+config = AutoConfig.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, config=config, torch_dtype=torch.bfloat16)
+model = PeftModel.from_pretrained(model, "McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp")
+model = model.merge_and_unload() # This can take several minutes
+
+# Loading supervised model. This loads the trained LoRA weights on top of MNTP model. Hence the final weights are -- Base model + MNTP (LoRA) + supervised (LoRA).
+model = PeftModel.from_pretrained(model, "McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-supervised")
+
+# Wrapper for encoding and pooling operations
+l2v = LLM2Vec(model, tokenizer, pooling_mode="mean", max_length=512)
+
+# Encoding queries using instructions
+instruction = 'Given a web search query, retrieve relevant passages that answer the query:'
+queries = [
+    [instruction, 'how much protein should a female eat'],
+    [instruction, 'summit define']
+]
+q_reps = l2v.encode(queries)
+
+# Encoding documents. Instruction are not required for documents
+documents = [
+    "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.",
+    "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."
+]
+d_reps = l2v.encode(documents)
+
+# Compute cosine similarity
+q_reps_norm = torch.nn.functional.normalize(q_reps, p=2, dim=1)
+d_reps_norm = torch.nn.functional.normalize(d_reps, p=2, dim=1)
+cos_sim = torch.mm(q_reps_norm, d_reps_norm.transpose(0, 1))
+
+print(cos_sim)
+"""
+tensor([[0.5486, 0.0554],
+        [0.0567, 0.5437]])
+"""
+```
+
+## Questions
+If you have any question about the code, feel free to email Parishad (`[email protected]`) and Vaibhav (`vaibhav.[email protected]`).