Instructions to use Roseshmay/led-qa-exaone-3.0-7.8b-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Roseshmay/led-qa-exaone-3.0-7.8b-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct") model = PeftModel.from_pretrained(base_model, "Roseshmay/led-qa-exaone-3.0-7.8b-lora") - Notebooks
- Google Colab
- Kaggle
LED QA LoRA โ 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/fullProposed 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)
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Model tree for Roseshmay/led-qa-exaone-3.0-7.8b-lora
Base model
LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct