---
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

```python
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)
```