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TODO #1

Llama2 λͺ¨λΈμ˜ 정확도λ₯Ό ν‘œλ‘œ λ‚˜νƒ€λ‚΄μ‹œμ˜€.

TP TN
PP 402 197
PN 47 354
  • ν•™μŠ΅ 900번 μŠ€ν… μˆ˜ν–‰ μ‹œμ˜ μ •ν™•λ„λŠ” (402 + 354) / 1000 = 0.756
  • 1600 μŠ€ν…μ„ μ‹œλ„ν•˜κ³  μ‹€νŒ¨ν•˜μ—¬ μ‹œν€€μŠ€ 길이λ₯Ό 400으둜 쀄여 λ³΄μ•˜λŠ”λ° 예츑 정확도가 ν•˜λ½ν•¨
  • midm λͺ¨λΈμ€ ν•™μŠ΅ 300번의 μŠ€ν…μ„ μˆ˜ν–‰ν–ˆκ³ , Llama2λŠ” 900번의 μŠ€ν…μ„ μ‹€ν–‰ν•˜μ˜€μœΌλ‚˜ 예츑 μ •ν™•λ„μ—λŠ” 큰 차이가 μ—†μ—ˆλ‹€.

library_name: peft base_model: meta-llama/Llama-2-7b-chat-hf

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Training procedure

The following bitsandbytes quantization config was used during training:

  • quant_method: bitsandbytes
  • load_in_8bit: False
  • load_in_4bit: True
  • llm_int8_threshold: 6.0
  • llm_int8_skip_modules: None
  • llm_int8_enable_fp32_cpu_offload: False
  • llm_int8_has_fp16_weight: False
  • bnb_4bit_quant_type: nf4
  • bnb_4bit_use_double_quant: False
  • bnb_4bit_compute_dtype: bfloat16

Framework versions

  • PEFT 0.7.0