README.md

metadata

library_name: peft
base_model: LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct
tags:
  - lora
  - qa
  - f1-regulations
  - led
pipeline_tag: text-generation

LED QA LoRA — EXAONE-3.0-7.8B-Instruct

LoRA adapter fine-tuned on F1 technical regulations QA.

Base model

LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct

Training

• Dataset: filtered/full Proposed QA SFT split
• LoRA r=16, alpha=32, target=all linear projections
• Epochs: 3
• Max seq len: 2048

Load

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = "LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct"
adapter = "REPO_ID"

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