--- license: other library_name: peft tags: - llama-factory - lora - generated_from_trainer base_model: /workspace/xll/checkpoints/lmsys/vicuna-7b-v1.5 model-index: - name: relation results: [] --- # relation This model is a fine-tuned version of [/workspace/xll/checkpoints/lmsys/vicuna-7b-v1.5](https://huggingface.co//workspace/xll/checkpoints/lmsys/vicuna-7b-v1.5) on the vicuna_relation_test dataset. It achieves the following results on the evaluation set: - Loss: 0.4715 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## 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: True - bnb_4bit_compute_dtype: float16 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 1200 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.0929 | 0.02 | 10 | 2.1679 | | 1.8074 | 0.04 | 20 | 1.4093 | | 1.2188 | 0.05 | 30 | 1.1259 | | 1.0841 | 0.07 | 40 | 0.9606 | | 0.9594 | 0.09 | 50 | 0.8682 | | 0.8765 | 0.11 | 60 | 0.8166 | | 0.852 | 0.12 | 70 | 0.7803 | | 0.8404 | 0.14 | 80 | 0.7602 | | 0.8183 | 0.16 | 90 | 0.7223 | | 0.7816 | 0.18 | 100 | 0.7134 | | 0.7792 | 0.19 | 110 | 0.7234 | | 0.7648 | 0.21 | 120 | 0.6883 | | 0.8132 | 0.23 | 130 | 0.7020 | | 0.7599 | 0.25 | 140 | 0.6684 | | 0.7518 | 0.26 | 150 | 0.6716 | | 0.7452 | 0.28 | 160 | 0.6634 | | 0.7215 | 0.3 | 170 | 0.6610 | | 0.7088 | 0.32 | 180 | 0.6598 | | 0.7237 | 0.33 | 190 | 0.6470 | | 0.7353 | 0.35 | 200 | 0.6315 | | 0.7111 | 0.37 | 210 | 0.6466 | | 0.7136 | 0.39 | 220 | 0.6329 | | 0.7044 | 0.4 | 230 | 0.6357 | | 0.7369 | 0.42 | 240 | 0.6215 | | 0.6995 | 0.44 | 250 | 0.6103 | | 0.7027 | 0.46 | 260 | 0.5964 | | 0.6872 | 0.47 | 270 | 0.6044 | | 0.7182 | 0.49 | 280 | 0.6127 | | 0.6897 | 0.51 | 290 | 0.6016 | | 0.6824 | 0.53 | 300 | 0.5880 | | 0.673 | 0.54 | 310 | 0.5902 | | 0.6956 | 0.56 | 320 | 0.5811 | | 0.6889 | 0.58 | 330 | 0.5850 | | 0.6773 | 0.6 | 340 | 0.5934 | | 0.6782 | 0.61 | 350 | 0.5942 | | 0.719 | 0.63 | 360 | 0.5849 | | 0.6809 | 0.65 | 370 | 0.5799 | | 0.6412 | 0.67 | 380 | 0.5710 | | 0.6411 | 0.69 | 390 | 0.5628 | | 0.6519 | 0.7 | 400 | 0.5612 | | 0.6446 | 0.72 | 410 | 0.5562 | | 0.6574 | 0.74 | 420 | 0.5645 | | 0.6633 | 0.76 | 430 | 0.5664 | | 0.6673 | 0.77 | 440 | 0.5483 | | 0.6466 | 0.79 | 450 | 0.5542 | | 0.653 | 0.81 | 460 | 0.5411 | | 0.6384 | 0.83 | 470 | 0.5362 | | 0.6287 | 0.84 | 480 | 0.5453 | | 0.661 | 0.86 | 490 | 0.5645 | | 0.608 | 0.88 | 500 | 0.5245 | | 0.6584 | 0.9 | 510 | 0.5376 | | 0.6416 | 0.91 | 520 | 0.5471 | | 0.6527 | 0.93 | 530 | 0.5426 | | 0.6164 | 0.95 | 540 | 0.5284 | | 0.611 | 0.97 | 550 | 0.5313 | | 0.614 | 0.98 | 560 | 0.5263 | | 0.6382 | 1.0 | 570 | 0.5317 | | 0.5804 | 1.02 | 580 | 0.5207 | | 0.6291 | 1.04 | 590 | 0.5238 | | 0.5911 | 1.05 | 600 | 0.5174 | | 0.6111 | 1.07 | 610 | 0.5281 | | 0.5578 | 1.09 | 620 | 0.5255 | | 0.6055 | 1.11 | 630 | 0.5177 | | 0.6015 | 1.12 | 640 | 0.5131 | | 0.6072 | 1.14 | 650 | 0.5168 | | 0.5956 | 1.16 | 660 | 0.5169 | | 0.6099 | 1.18 | 670 | 0.5170 | | 0.6038 | 1.19 | 680 | 0.5056 | | 0.583 | 1.21 | 690 | 0.5121 | | 0.5885 | 1.23 | 700 | 0.5234 | | 0.5784 | 1.25 | 710 | 0.5028 | | 0.5744 | 1.26 | 720 | 0.5100 | | 0.6014 | 1.28 | 730 | 0.5038 | | 0.6185 | 1.3 | 740 | 0.5146 | | 0.6184 | 1.32 | 750 | 0.5317 | | 0.6141 | 1.34 | 760 | 0.5080 | | 0.6146 | 1.35 | 770 | 0.5165 | | 0.5721 | 1.37 | 780 | 0.5040 | | 0.5931 | 1.39 | 790 | 0.4934 | | 0.5944 | 1.41 | 800 | 0.4876 | | 0.6002 | 1.42 | 810 | 0.4930 | | 0.5557 | 1.44 | 820 | 0.4913 | | 0.58 | 1.46 | 830 | 0.4910 | | 0.5459 | 1.48 | 840 | 0.4884 | | 0.5871 | 1.49 | 850 | 0.4860 | | 0.5554 | 1.51 | 860 | 0.4857 | | 0.5819 | 1.53 | 870 | 0.4649 | | 0.5649 | 1.55 | 880 | 0.4790 | | 0.5779 | 1.56 | 890 | 0.4807 | | 0.5756 | 1.58 | 900 | 0.4834 | | 0.5563 | 1.6 | 910 | 0.4946 | | 0.5393 | 1.62 | 920 | 0.4848 | | 0.5551 | 1.63 | 930 | 0.4845 | | 0.5687 | 1.65 | 940 | 0.4807 | | 0.5469 | 1.67 | 950 | 0.4749 | | 0.5771 | 1.69 | 960 | 0.4859 | | 0.5689 | 1.7 | 970 | 0.4734 | | 0.5741 | 1.72 | 980 | 0.4882 | | 0.5643 | 1.74 | 990 | 0.4816 | | 0.5603 | 1.76 | 1000 | 0.4676 | | 0.5925 | 1.77 | 1010 | 0.4686 | | 0.5834 | 1.79 | 1020 | 0.4743 | | 0.5902 | 1.81 | 1030 | 0.4916 | | 0.5777 | 1.83 | 1040 | 0.4748 | | 0.5921 | 1.84 | 1050 | 0.4843 | | 0.5877 | 1.86 | 1060 | 0.4742 | | 0.5453 | 1.88 | 1070 | 0.4705 | | 0.5445 | 1.9 | 1080 | 0.4663 | | 0.5686 | 1.92 | 1090 | 0.4745 | | 0.5712 | 1.93 | 1100 | 0.4888 | | 0.6032 | 1.95 | 1110 | 0.4861 | | 0.5491 | 1.97 | 1120 | 0.4721 | | 0.5452 | 1.99 | 1130 | 0.4645 | | 0.5526 | 2.0 | 1140 | 0.4877 | | 0.5443 | 2.02 | 1150 | 0.4716 | | 0.5103 | 2.04 | 1160 | 0.4632 | | 0.5202 | 2.06 | 1170 | 0.4802 | | 0.5436 | 2.07 | 1180 | 0.4681 | | 0.5454 | 2.09 | 1190 | 0.4709 | | 0.5183 | 2.11 | 1200 | 0.4742 | ### Framework versions - PEFT 0.7.0 - Transformers 4.37.1 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1