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Gorani Model Card

์†Œ๊ฐœ (Introduce)

์ด ๋ชจ๋ธ์€ ๋ฒˆ์—ญ์„ ์œ„ํ•œ ๋ชจ๋ธ์ž…๋‹ˆ๋‹ค. ํ•œ๊ตญ ๊ณ ์œ ์–ด์˜ ์ •ํ™•ํ•œ ๋ฒˆ์—ญ์„ ์ƒ์„ฑํ•˜๊ธฐ ์œ„ํ•ด ํ•œ๊ตญ์–ด, ์˜์–ด, ์ผ๋ณธ์–ด์˜ ์–ธ์–ด ๋ฐ์ดํ„ฐ๋ฅผ ํ˜ผํ•ฉํ•˜์—ฌ unsloth/gemma-2b-it-bnb-4bit์„ ํ•™์Šต์‹œ์ผœ ์ƒ์„ฑ๋œ gorani-2B ์ž…๋‹ˆ๋‹ค.
gorani๋Š” ํ˜„์žฌ ํ•œ๊ตญ์–ด, ์˜์–ด, ์ผ๋ณธ์–ด๋งŒ ๋ฒˆ์—ญ์„ ์ง€์›ํ•ฉ๋‹ˆ๋‹ค.

๋ชจ๋ธ ์ •๋ณด

  • ๊ฐœ๋ฐœ์ž: taeyoon0620
  • ๋ชจ๋ธ ์œ ํ˜•: gemma๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋Š” 2B ๋งค๊ฐœ๋ณ€์ˆ˜ ๋ชจ๋ธ์ธ gorani-2B
  • ์ง€์› ์–ธ์–ด: ํ•œ๊ตญ์–ด, ์˜์–ด, ์ผ๋ณธ์–ด
  • ๋ผ์ด์„ผ์Šค: gemma

Training Hyperparameters

  • per_device_train_batch_size: 8
  • gradient_accumulation_steps: 4
  • warmup_steps: 5
  • learning_rate: 2e-5
  • fp16: not is_bfloat16_supported()
  • num_train_epochs: 3
  • weight_decay: 0.01
  • lr_scheduler_type: "linear"

ํ•™์Šต ๋ฐ์ดํ„ฐ

๋ฐ์ดํ„ฐ์…‹ ๋งํฌ

ํ•™์Šต ์„ฑ๋Šฅ ๋น„๊ต

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Step Training Loss Validation Loss
50 0.856900 1.243296
100 0.637300 1.080636
150 0.561300 1.015324
200 0.523100 0.972539
250 0.491600 0.951547
300 0.485000 0.942520
350 0.457100 0.928466
400 0.482600 0.913566
450 0.445000 0.915080
500 0.447300 0.900509
550 0.443200 0.896823
600 0.420800 0.898253
650 0.430200 0.888035
700 0.428700 0.884415
750 0.416400 0.875832
800 0.397400 0.877212
850 0.403800 0.878696
900 0.410400 0.873603
950 0.423900 0.876391
1000 0.412100 0.874712
1050 0.404000 0.878219
1100 0.391000 0.868475
1150 0.383800 0.867502
1200 0.388800 0.877054
1250 0.382200 0.864894
1300 0.389500 0.862716
1350 0.401100 0.857156
1400 0.364600 0.865956
1450 0.386700 0.858560
1500 0.379200 0.860608
1550 0.370000 0.864580
1600 0.367900 0.863778
1650 0.360100 0.866951
1700 0.372300 0.867361
1750 0.378400 0.867690
1800 0.371100 0.863734
1850 0.376400 0.862673
1900 0.364200 0.862987
1950 0.368000 0.865702
2000 0.374900 0.865652
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