Organic SFT (Human-SFT) - LLaMA 3.1 8B LoRA
This is a LoRA adapter for science communication, trained on human-written scientific explanations from Reddit r/AskScience.
Model Description
- Base Model:
meta-llama/Llama-3.1-8B-Instruct - Training Data: Human-written answers from r/AskScience (organic/real data)
- Training Method: Supervised Fine-Tuning (SFT) with LoRA
- Purpose: Generate high-quality scientific explanations
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base = "meta-llama/Llama-3.1-8B-Instruct"
adapter = "mattany/organic-sft-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))
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meta-llama/Llama-3.1-8B Finetuned
meta-llama/Llama-3.1-8B-Instruct