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README.md

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+    ## 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.
+
+    ### 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
+
+    ### Key Features
+
+    - Rapid generation
+    - Only requires target sequence
+    - Outputs diverse, human-like antibodies
+
+    ### 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]
+
+    ```