README.md

metadata

license: apache-2.0
base_model:
  - meta-llama/Llama-3.3-70B-Instruct
  - unsloth/Meta-Llama-3.1-8B-Instruct
tags:
  - biology
  - chemistry

Pro-1-preview

Pro-1 is a reasoning model trained using GRPO towards a physics based reward function for protein stability.

It takes in a protein sequence + text description of the protein + effects of previous engineering attempts, reasons over the information given, and proposes modifications to improve the stability of the given sequence.

LORA checkpoints

Model	Checkpoint
8b base GRPO	best-checkpoint
8b creative reward	creativity-lm-grpo-mega-run-full
8b creative + specificity reward (default)	all-lm-grpo-mega-run
70b SFT only	llama_70b_4bit_sft_lora_model

Example Usage

from unsloth import FastLanguageModel
from transformers import TextIteratorStreamer
import threading

def run_protein_engineering_example():
    # Load the model and tokenizer
    model, tokenizer = FastLanguageModel.from_pretrained(
        model_name="unsloth/meta-Llama-3.1-8B-Instruct",
        max_seq_length=32768,
        load_in_4bit=True,
        fast_inference=True,
        max_lora_rank=32,
        gpu_memory_utilization=0.6,
    )
    
    # Load the protein engineering adapter weights
    model.load_adapter("your-username/protein-engineering-llama-3.1")
    FastLanguageModel.for_inference(model)
    
    protein_sequence = "MSHHWGYGKHNGPEHWHKDFPIAKGERQSPVDIDTHTAKYDPSLKPLSVSYDQATSLRILNNGHAFNVEFDDSQDKAVLKGGPLDGTY"
    
    prompt = f"""

...{STRUCTURED PROMPT SEE https://github.com/michaelhla/pro-1 FOR CORRECT USAGE}...

"""

    # Initialize the streamer for text generation
    streamer = TextIteratorStreamer(tokenizer, skip_special_tokens=True)
    
    # Set up generation parameters
    generation_kwargs = dict(
        input_ids=tokenizer(prompt, return_tensors="pt").input_ids.to(model.device),
        streamer=streamer,
        max_new_tokens=4096,
        temperature=0.9,
        top_p=0.95,
        do_sample=True
    )
    
    # Create a thread to run the generation
    thread = threading.Thread(target=model.generate, kwargs=generation_kwargs)
    thread.start()
    
    # Print the response as it streams
    print("Model response (streaming):")
    for new_text in streamer:
        print(new_text, end="", flush=True)
    
    thread.join()  # Ensure generation is complete

if __name__ == "__main__":
    run_protein_engineering_example()

Note: While the model was specifically trained on enzymes, it should work for any protein sequence. Curious to hear if this is true!

Disclaimer: This is a preview version and as a result the model can be very dumb. Always double check sure your modified sequences have the correct mutations applied. Assume all references from the model are hallucinated.