This repository contains LoRA/PEFT adapters trained on TRUBA (HPC) with a focus on Turkish.
It does not include the full weighting; during use, the base model meta-llama/Meta-Llama-3-8B-Instruct (requires license approval) is loaded and this adapter is applied.

Quick Start (Transformers + PEFT)

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel

BASE = "meta-llama/Meta-Llama-3-8B-Instruct"  # lisans şartlarını hesabınızda kabul edin
ADAPTER = "KayaKoray/LLAMA-3-8B-Instruct-Eco-Tourism-Planner"

quant = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16)
tok = AutoTokenizer.from_pretrained(BASE, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(BASE, device_map="auto",
                                             torch_dtype=torch.bfloat16,
                                             quantization_config=quant)
model = PeftModel.from_pretrained(model, ADAPTER)

prompt = "Türkiye'de sürdürülebilir turizm için 3 somut öneri ver."
out = model.generate(**tok(prompt, return_tensors="pt").to(model.device),
                     max_new_tokens=200, do_sample=True, temperature=0.7, top_p=0.95)
print(tok.decode(out[0], skip_special_tokens=True))