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---
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
[](https://github.com/michaelhla/pro-1)
[](https://twitter.com/hla_michael)
[](https://huggingface.co/mhla/pro-1)
[](https://michaelhla.com/blog/pro1)
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](https://huggingface.co/mhla/pro-1/tree/main/best-checkpoint) |
| 8b creative reward | [creativity-lm-grpo-mega-run-full](https://huggingface.co/mhla/pro-1/tree/main/creativity-lm-grpo-mega-run-full) |
| 8b creative + specificity reward (default) | [all-lm-grpo-mega-run](https://huggingface.co/mhla/pro-1/tree/main/all-lm-grpo-mega-run-full) |
| 70b SFT only | [llama_70b_4bit_sft_lora_model](https://huggingface.co/mhla/pro-1/tree/main/llama_70b_4bit_sft_lora_model) |
## Example Usage
```python
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.
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