Base Model: meta-llama/Llama-3.1-8B-Instruct
Training Data: Human preference pairs from r/AskScience (organic/real data)
Training Method: SFT followed by DPO with LoRA
Purpose: Generate high-quality scientific explanations aligned with human preferences

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = "meta-llama/Llama-3.1-8B-Instruct"
adapter = "mattany/organic-dpo-3.1-8B-lora"

tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, device_map="auto", torch_dtype="auto")
model = PeftModel.from_pretrained(model, adapter)

prompt = "What is photosynthesis?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))