organic-dpo-3.1-8B-lora

Organic DPO (SFT + Human-DPO) - LLaMA 3.1 8B LoRA

This is a LoRA adapter for science communication, trained with Direct Preference Optimization (DPO) using human preference data.

Model Description

• 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

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))