Update README.md

README.md

@@ -1,3 +1,89 @@
----
-license: apache-2.0
----
+---
+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](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.