Instructions to use jamesjje/mistral7b-arc-challenge-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use jamesjje/mistral7b-arc-challenge-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1") model = PeftModel.from_pretrained(base_model, "jamesjje/mistral7b-arc-challenge-lora") - Notebooks
- Google Colab
- Kaggle
Mistral-7B ARC-Challenge LoRA (E6b)
mistralai/Mistral-7B-v0.1์ ARC-Challenge ์ฑ๋ฅ ํฅ์์ ์ํด QLoRA๋ก ํ์ธํ๋ํ LoRA ์ด๋ํฐ.
ํ๊ฐ ๊ฒฐ๊ณผ (lm-evaluation-harness, arc_challenge, 25-shot)
| ๋ชจ๋ธ | acc | acc_norm |
|---|---|---|
| Mistral-7B-v0.1 (base) | - | 0.6143 |
| ๋ณธ ์ด๋ํฐ (E6b) | 0.6399 | 0.6741 |
base ๋๋น acc_norm +5.98%p.
ํ์ต ๋ฐ์ดํฐ
- ๊ณต๊ฐ ๊ณผํ QA: ARC-Challenge/Easy(train), OpenBookQA, SciQ
- ํฉ์ฑ ๋ฐ์ดํฐ: AceMath ๋ ์ํผ ๊ธฐ๋ฐ์ผ๋ก ์์ฑํ ๊ณผํ MCQA (์ผ์ค ์ผ์น ๊ฒ์ฆ + ๋์ปจํ๋ฏธ๋ค์ด์ )
- ๋์ด๋ ํํฐ ์ ์ฉ, ARC test/validation ๋์ ๋์ปจํ๋ฏธ๋ค์ด์ ์ํ
์ฌ์ฉ๋ฒ
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", torch_dtype="float16", device_map="auto")
model = PeftModel.from_pretrained(base, "jamesjje/mistral7b-arc-challenge-lora")
tok = AutoTokenizer.from_pretrained("jamesjje/mistral7b-arc-challenge-lora")
์ฐธ๊ณ
๋ณํฉ(merge_and_unload) ํ ์ ์ฅํ ๋ชจ๋ธ์์ ์ฑ๋ฅ ์ ํ๊ฐ ๊ด์ธก๋์ด, ๋ณธ ๋ ํฌ๋ ์ด๋ํฐ๋ฅผ ๋ฐฐํฌํ๋ค.
ํ๊ฐ ์ lm_eval --model hf --model_args pretrained=mistralai/Mistral-7B-v0.1,peft=jamesjje/mistral7b-arc-challenge-lora ํํ๋ก ์ฌํํ ์ ์๋ค.
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mistralai/Mistral-7B-v0.1