Update README.md

README.md

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-## A Fine-Tuned GPT for De Novo Therapeutic Antibodies
+## AntibodyGPT: A Fine-Tuned GPT for De Novo Therapeutic Antibodies
 
-### What are antibodies?
+Antibodies are proteins that bind to a target protein (called an antigen) in order to mount an immune response. 
+They are incredibly **safe** and **effective** therapeutics against infectious diseases, cancer, and autoimmune disorders.
 
-Antibodies are proteins that bind to a target protein (called an antigen) in order to mount an immune response. They are incredibly **safe** and **effective** therapeutics against infectious diseases, cancer, and autoimmune disorders.
+Current antibody discovery methods require a lot of capital, expertise, and luck. Generative AI opens up the possibility of 
+moving from a paradigm of antibody discovery to antibody generation. However, work is required to translate the advances of LLMs to the realm of drug discovery.
 
-### Why aren’t there more antibodies on the market?
-
-Current antibody discovery methods require a lot of capital, expertise, and luck. Generative AI opens up the possibility of moving from a paradigm of antibody discovery to antibody generation. However, work is required to translate the advances of LLMs to the realm of drug discovery.
-
-### What is AntibodyGPT?
-
-A fine-tuned GPT language model that researchers can use to rapidly generate functional, diverse antibodies for any given target sequence
+AntibodyGPT is a fine-tuned GPT language model that researchers can use to rapidly generate functional, diverse antibodies for any given target sequence
 
 ### Key Features
 
@@ -30,48 +26,3 @@ A fine-tuned GPT language model that researchers can use to rapidly generate fun
 ### Links: 
 - [Web Demo](https://orca-app-ygzbp.ondigitalocean.app/Demo_Antibody_Generator)
 - [Huggingface Model Repository](https://huggingface.co/AntibodyGeneration)
-- [OpenSource RunPod Severless Rest API](https://github.com/joethequant/docker_protein_generator)
-- [The Code for this App](https://github.com/joethequant/docker_streamlit_antibody_protein_generation)
-
-### Additional Resources and Links
-- [Progen Foundation Models](https://github.com/salesforce/progen)
-- [ANARCI Github](https://github.com/oxpig/ANARCI)
-- [ANARCI Webserver](http://opig.stats.ox.ac.uk/webapps/anarci/)
-- [TAP: Therapeutic Antibody Profiler](https://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/sabpred/tap)
-- [ESM Fold](https://esmatlas.com/resources?action=fold)
-
-
-
-### Example Code To Use AntibodyGPT
-
-```python
-from models.progen.modeling_progen import ProGenForCausalLM
-import torch
-from tokenizers import Tokenizer
-import json
-
-# Define the model identifier from Hugging Face's model hub
-model_path = 'AntibodyGeneration/fine-tuned-progen2-small'
-
-# Load the model and tokenizer
-model = ProGenForCausalLM.from_pretrained(model_path)
-tokenizer = Tokenizer.from_file('tokenizer.json')
-
-# Define your sequence and other parameters
-target_sequence = 'MQIPQAPWPVVWAVLQLGWRPGWFLDSPDRPWNPPTFSPALLVVTEGDNATFTCSFSNTSESFVLNWYRMSPSNQTDKLAAFPEDRSQPGQDCRFRVTQLPNGRDFHMSVVRARRNDSGTYLCGAISLAPKAQIKESLRAELRVTERRAEVPTAHPSPSPRPAGQFQTLVVGVVGGLLGSLVLLVWVLAVICSRAARGTIGARRTGQPLKEDPSAVPVFSVDYGELDFQWREKTPEPPVPCVPEQTEYATIVFPSGMGTSSPARRGSADGPRSAQPLRPEDGHCSWPL'
-number_of_sequences = 2
-
-# Tokenize the sequence
-tokenized_sequence = tokenizer(target_sequence, return_tensors="pt")
-
-# Move model and tensors to CUDA if available
-device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
-model = model.to(device)
-tokenized_sequence = tokenized_sequence.to(device)
-
-# Generate sequences
-with torch.no_grad():
-  output = model.generate(**tokenized_sequence, max_length=1024, pad_token_id=tokenizer.pad_token_id, do_sample=True, top_p=0.9, temperature=0.8, num_return_sequences=number_of_sequences)
-
-# Decoding the output to get generated sequences
-generated_sequences = [tokenizer.decode(output_seq, skip_special_tokens=True) for output_seq in output]