Update README.md

README.md

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-    ## A Fine-Tuned GPT for De Novo Therapeutic Antibodies
+## A Fine-Tuned GPT for De Novo Therapeutic Antibodies
 
-    ### What are antibodies?
+### What are antibodies?
 
-    Antibodies are proteins that bind to a target protein (called an antigen) in order to mount an immune response
+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.
+They are incredibly **safe** and **effective** therapeutics against infectious diseases, cancer, and autoimmune disorders.
 
-    ### Why aren’t there more antibodies on the market?
+### Why aren’t there more antibodies on the market?
 
-    Current antibody discovery methods require a lot of capital, expertise, and luck.
+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.
+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.
+However, work is required to translate the advances of LLMs to the realm of drug discovery.
 
-    ### What is AntibodyGPT?
+### What is AntibodyGPT?
 
-    A fine-tuned GPT language model that researchers can use to rapidly generate functional, diverse antibodies for any given target sequence
+A fine-tuned GPT language model that researchers can use to rapidly generate functional, diverse antibodies for any given target sequence
 
-    ### Key Features
+### Key Features
 
-    - Rapid generation
-    - Only requires target sequence
-    - Outputs diverse, human-like antibodies
+- 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)
+### 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)
+### 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
+### Example Code To Use AntibodyGPT
 
-    # Define the model identifier from Hugging Face's model hub
-    model_path = 'AntibodyGeneration/fine-tuned-progen2-small'
+```python
+from models.progen.modeling_progen import ProGenForCausalLM
+import torch
+from tokenizers import Tokenizer
+import json
 
-    # Load the model and tokenizer
-    model = ProGenForCausalLM.from_pretrained(model_path)
-    tokenizer = Tokenizer.from_file('tokenizer.json')
+# Define the model identifier from Hugging Face's model hub
+model_path = 'AntibodyGeneration/fine-tuned-progen2-small'
 
-    # Define your sequence and other parameters
-    target_sequence = 'MQIPQAPWPVVWAVLQLGWRPGWFLDSPDRPWNPPTFSPALLVVTEGDNATFTCSFSNTSESFVLNWYRMSPSNQTDKLAAFPEDRSQPGQDCRFRVTQLPNGRDFHMSVVRARRNDSGTYLCGAISLAPKAQIKESLRAELRVTERRAEVPTAHPSPSPRPAGQFQTLVVGVVGGLLGSLVLLVWVLAVICSRAARGTIGARRTGQPLKEDPSAVPVFSVDYGELDFQWREKTPEPPVPCVPEQTEYATIVFPSGMGTSSPARRGSADGPRSAQPLRPEDGHCSWPL'
-    number_of_sequences = 2
+# Load the model and tokenizer
+model = ProGenForCausalLM.from_pretrained(model_path)
+tokenizer = Tokenizer.from_file('tokenizer.json')
 
-    # Tokenize the sequence
-    tokenized_sequence = tokenizer(target_sequence, return_tensors="pt")
+# Define your sequence and other parameters
+target_sequence = 'MQIPQAPWPVVWAVLQLGWRPGWFLDSPDRPWNPPTFSPALLVVTEGDNATFTCSFSNTSESFVLNWYRMSPSNQTDKLAAFPEDRSQPGQDCRFRVTQLPNGRDFHMSVVRARRNDSGTYLCGAISLAPKAQIKESLRAELRVTERRAEVPTAHPSPSPRPAGQFQTLVVGVVGGLLGSLVLLVWVLAVICSRAARGTIGARRTGQPLKEDPSAVPVFSVDYGELDFQWREKTPEPPVPCVPEQTEYATIVFPSGMGTSSPARRGSADGPRSAQPLRPEDGHCSWPL'
+number_of_sequences = 2
 
-    # 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)
+# Tokenize the sequence
+tokenized_sequence = tokenizer(target_sequence, return_tensors="pt")
 
-    # 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)
+# 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)
 
-    # Decoding the output to get generated sequences
-    generated_sequences = [tokenizer.decode(output_seq, skip_special_tokens=True) for output_seq in output]
+# 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]