How to use from the
Use from the
PEFT library
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")

Mistral-7B ARC-Challenge LoRA (E6b)

mistralai/Mistral-7B-v0.1์„ ARC-Challenge ์„ฑ๋Šฅ ํ–ฅ์ƒ์„ ์œ„ํ•ด QLoRA๋กœ ํŒŒ์ธํŠœ๋‹ํ•œ LoRA ์–ด๋Œ‘ํ„ฐ.

ํ‰๊ฐ€ ๊ฒฐ๊ณผ (lm-evaluation-harness, arc_challenge, 25-shot)

๋ชจ๋ธ acc acc_norm
Mistral-7B-v0.1 (base) - 0.6143
๋ณธ ์–ด๋Œ‘ํ„ฐ (E6b) 0.6399 0.6741

base ๋Œ€๋น„ acc_norm +5.98%p.

ํ•™์Šต ๋ฐ์ดํ„ฐ

  • ๊ณต๊ฐœ ๊ณผํ•™ QA: ARC-Challenge/Easy(train), OpenBookQA, SciQ
  • ํ•ฉ์„ฑ ๋ฐ์ดํ„ฐ: AceMath ๋ ˆ์‹œํ”ผ ๊ธฐ๋ฐ˜์œผ๋กœ ์ƒ์„ฑํ•œ ๊ณผํ•™ MCQA (์‚ผ์ค‘ ์ผ์น˜ ๊ฒ€์ฆ + ๋””์ปจํƒœ๋ฏธ๋„ค์ด์…˜)
  • ๋‚œ์ด๋„ ํ•„ํ„ฐ ์ ์šฉ, ARC test/validation ๋Œ€์ƒ ๋””์ปจํƒœ๋ฏธ๋„ค์ด์…˜ ์ˆ˜ํ–‰

์‚ฌ์šฉ๋ฒ•

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 

๋ณ‘ํ•ฉ(merge_and_unload) ํ›„ ์ €์žฅํ•œ ๋ชจ๋ธ์—์„œ ์„ฑ๋Šฅ ์ €ํ•˜๊ฐ€ ๊ด€์ธก๋˜์–ด, ๋ณธ ๋ ˆํฌ๋Š” ์–ด๋Œ‘ํ„ฐ๋ฅผ ๋ฐฐํฌํ•œ๋‹ค. ํ‰๊ฐ€ ์‹œ lm_eval --model hf --model_args pretrained=mistralai/Mistral-7B-v0.1,peft=jamesjje/mistral7b-arc-challenge-lora ํ˜•ํƒœ๋กœ ์žฌํ˜„ํ•  ์ˆ˜ ์žˆ๋‹ค.

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