Update README.md

Added additional Information

README.md

@@ -63,6 +63,55 @@ Performance and analytics:
 ## How to Use
 Instructions on how to use the model, including example prompts and API documentation, are available in the [Code Repository](https://github.com/joethequant/docker_streamlit_antibody_protein_generation).
 
+### Example Code 
+
+```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]
+```
+
+## Links: 
+    
+- [Huggingface Model Repository](https://huggingface.co/AntibodyGeneration)
+- [Web Demo](https://orca-app-ygzbp.ondigitalocean.app/Demo_Antibody_Generator)
+- [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)
+  
 ## Limitations and Future Work
 - Predictions require experimental validation for practical use.
 - Future improvements will focus on incorporating diverse training data and enhancing prediction accuracy for the efficacy of generated antibodies.