KT-AI-midm-bitext-S-7B-inst-v1

Accuracy

결과 분석

상위 1000개의 test dataset을 이용해 성능 확인

• True Positive: 452

• True Negative: 440

• False Positive: 41

• False Negative: 43

• precision: 0.9168356997971603

• recall: 0.9131313131313131

• f1 score: 0.9149797570850203

'긍정'과 '부정'에 포함되지 않은 단어로 예측한 경우

• ' ', '정'으로 예측: 총 24개

--> 오분류가 존재하지만, 정확도에 있어서 90% 이상의 성능을 보이고 있고, llama-2보다 우수한 성능을 보인다.

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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 0.6.2