Updated readme

README.md

@@ -6,3 +6,54 @@
   <img src="https://github.com/McGill-NLP/llm2vec/assets/12207571/48efd48a-431b-4625-8e0f-248a442e3839?raw=true" width="75%" alt="LLM2Vec_figure1"/>
 </p>
 
+## Instrallation
+To use LLM2Vec, first install the llm2vec package from PyPI.
+
+```bash
+pip install llm2vec
+```
+You can also directly install it from our code by cloning the repository and: 
+
+```bash
+pip install -e .
+```
+
+## Getting Started
+LLM2Vec is a generic model, which takes a `tokenizer` and a `model`. First, we define the model and tokenizer using `transformers` library:
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModel, AutoConfig
+config = AutoConfig.from_pretrained("McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp", trust_remote_code=True)
+model = AutoModel.from_pretrained("McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp", trust_remote_code=True, config=config, torch_dtype=torch.bfloat16)
+tokenizer = AutoTokenizer.from_pretrained("McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp")
+```
+
+Then, we define our llm2vec model as follows:
+
+```python
+from llm2vec import LLM2Vec
+
+l2v = LLM2Vec(model, tokenizer)
+```
+
+This model now returns the text embedding for any input in the form of `[[instruction, text]]`.
+
+```python
+inputs = [
+  ['Retrieve duplicate questions from StackOverflow forum', 'Python (Numpy) array sorting'],
+  ['', 'Sort a list in python'],
+  ['', 'Sort an array in Java'],
+]
+repr = l2v.encode(inputs, convert_to_tensor=True)
+sim_pos = torch.nn.functional.cosine_similarity(repr[0].unsqueeze(0), repr[1].unsqueeze(0))  # tensor([0.5987])
+sim_neg = torch.nn.functional.cosine_similarity(repr[0].unsqueeze(0), repr[2].unsqueeze(0))  # tensor([0.5585])
+```
+
+# Model List
+
+# Training 
+Training code will be available soon.
+
+# Bugs or questions?
+If you have any question about the code, feel free to email Parishad (`[email protected]`) and Vaibhav (`[email protected]`).