--- base_model: meta-llama/Llama-2-13b-hf tags: - generated_from_trainer model-index: - name: qlora-out results: [] --- [Built with Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) # qlora-out This model is a fine-tuned version of [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5302 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0004 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.7438 | 0.01 | 20 | 0.6827 | | 0.8518 | 0.01 | 40 | 0.6707 | | 0.6798 | 0.02 | 60 | 0.6511 | | 0.7868 | 0.03 | 80 | 0.6231 | | 0.8218 | 0.04 | 100 | 0.6232 | | 0.6862 | 0.04 | 120 | 0.6324 | | 0.4989 | 0.05 | 140 | 0.6007 | | 0.6064 | 0.06 | 160 | 0.6127 | | 0.5355 | 0.06 | 180 | 0.6297 | | 0.6274 | 0.07 | 200 | 0.6211 | | 0.5512 | 0.08 | 220 | 0.6290 | | 0.727 | 0.08 | 240 | 0.6028 | | 0.5253 | 0.09 | 260 | 0.5971 | | 0.7679 | 0.1 | 280 | 0.5908 | | 0.4804 | 0.11 | 300 | 0.6154 | | 0.5801 | 0.11 | 320 | 0.5968 | | 0.3603 | 0.12 | 340 | 0.7127 | | 0.5948 | 0.13 | 360 | 0.5911 | | 0.7988 | 0.13 | 380 | 0.6060 | | 0.6002 | 0.14 | 400 | 0.6303 | | 0.5522 | 0.15 | 420 | 0.6124 | | 0.558 | 0.16 | 440 | 0.6061 | | 0.393 | 0.16 | 460 | 0.6050 | | 0.739 | 0.17 | 480 | 0.5977 | | 0.6462 | 0.18 | 500 | 0.5904 | | 0.5305 | 0.18 | 520 | 0.5852 | | 0.4749 | 0.19 | 540 | 0.5974 | | 0.832 | 0.2 | 560 | 0.5918 | | 0.7155 | 0.21 | 580 | 0.5857 | | 0.7374 | 0.21 | 600 | 0.6092 | | 1.2165 | 0.22 | 620 | 0.6071 | | 0.4901 | 0.23 | 640 | 0.5908 | | 0.4585 | 0.23 | 660 | 0.6041 | | 0.6474 | 0.24 | 680 | 0.5984 | | 0.4136 | 0.25 | 700 | 0.5860 | | 0.602 | 0.25 | 720 | 0.6251 | | 0.405 | 0.26 | 740 | 0.6182 | | 0.5059 | 0.27 | 760 | 0.5894 | | 0.4249 | 0.28 | 780 | 0.5800 | | 0.4847 | 0.28 | 800 | 0.5805 | | 0.4709 | 0.29 | 820 | 0.6140 | | 0.4279 | 0.3 | 840 | 0.5877 | | 0.7142 | 0.3 | 860 | 0.5801 | | 1.0536 | 0.31 | 880 | 0.6102 | | 0.694 | 0.32 | 900 | 0.5812 | | 0.5034 | 0.33 | 920 | 0.5833 | | 0.4208 | 0.33 | 940 | 0.5803 | | 0.5917 | 0.34 | 960 | 0.5756 | | 0.4655 | 0.35 | 980 | 0.5706 | | 0.4274 | 0.35 | 1000 | 0.5675 | | 0.3711 | 0.36 | 1020 | 0.5791 | | 0.6792 | 0.37 | 1040 | 0.5773 | | 0.3607 | 0.38 | 1060 | 0.5838 | | 0.5336 | 0.38 | 1080 | 0.5744 | | 0.5509 | 0.39 | 1100 | 0.5854 | | 0.2759 | 0.4 | 1120 | 0.5675 | | 0.5058 | 0.4 | 1140 | 0.5790 | | 0.4446 | 0.41 | 1160 | 0.5893 | | 0.4757 | 0.42 | 1180 | 0.5764 | | 0.4153 | 0.42 | 1200 | 0.5707 | | 0.5369 | 0.43 | 1220 | 0.5729 | | 0.4785 | 0.44 | 1240 | 0.5735 | | 0.4335 | 0.45 | 1260 | 0.5821 | | 0.5452 | 0.45 | 1280 | 0.5621 | | 0.3461 | 0.46 | 1300 | 0.5615 | | 0.5579 | 0.47 | 1320 | 0.5769 | | 0.6048 | 0.47 | 1340 | 0.5855 | | 0.6253 | 0.48 | 1360 | 0.5590 | | 0.5084 | 0.49 | 1380 | 0.5822 | | 0.3838 | 0.5 | 1400 | 0.5604 | | 0.667 | 0.5 | 1420 | 0.5622 | | 0.681 | 0.51 | 1440 | 0.5632 | | 0.3593 | 0.52 | 1460 | 0.5578 | | 0.4509 | 0.52 | 1480 | 0.5609 | | 0.4752 | 0.53 | 1500 | 0.5494 | | 0.3152 | 0.54 | 1520 | 0.5541 | | 0.3699 | 0.55 | 1540 | 0.5449 | | 0.3009 | 0.55 | 1560 | 0.5656 | | 0.2867 | 0.56 | 1580 | 0.5499 | | 0.7261 | 0.57 | 1600 | 0.5490 | | 0.5149 | 0.57 | 1620 | 0.5565 | | 0.473 | 0.58 | 1640 | 0.5445 | | 0.9732 | 0.59 | 1660 | 0.5505 | | 0.9606 | 0.59 | 1680 | 0.5471 | | 0.2714 | 0.6 | 1700 | 0.5651 | | 0.4927 | 0.61 | 1720 | 0.5527 | | 0.6928 | 0.62 | 1740 | 0.5433 | | 0.3776 | 0.62 | 1760 | 0.5507 | | 0.4636 | 0.63 | 1780 | 0.5443 | | 0.43 | 0.64 | 1800 | 0.5527 | | 0.5656 | 0.64 | 1820 | 0.5478 | | 0.729 | 0.65 | 1840 | 0.5542 | | 0.4355 | 0.66 | 1860 | 0.5411 | | 0.377 | 0.67 | 1880 | 0.5426 | | 0.5345 | 0.67 | 1900 | 0.5434 | | 0.4042 | 0.68 | 1920 | 0.5383 | | 0.3676 | 0.69 | 1940 | 0.5372 | | 0.4758 | 0.69 | 1960 | 0.5411 | | 0.4919 | 0.7 | 1980 | 0.5353 | | 0.2312 | 0.71 | 2000 | 0.5351 | | 0.7224 | 0.71 | 2020 | 0.5364 | | 0.3617 | 0.72 | 2040 | 0.5357 | | 0.8601 | 0.73 | 2060 | 0.5402 | | 0.3218 | 0.74 | 2080 | 0.5309 | | 0.3611 | 0.74 | 2100 | 0.5412 | | 0.4466 | 0.75 | 2120 | 0.5432 | | 0.5551 | 0.76 | 2140 | 0.5345 | | 0.4047 | 0.76 | 2160 | 0.5321 | | 0.4624 | 0.77 | 2180 | 0.5357 | | 0.5704 | 0.78 | 2200 | 0.5325 | | 0.715 | 0.79 | 2220 | 0.5313 | | 0.4913 | 0.79 | 2240 | 0.5300 | | 0.3605 | 0.8 | 2260 | 0.5294 | | 0.234 | 0.81 | 2280 | 0.5318 | | 0.6128 | 0.81 | 2300 | 0.5365 | | 0.236 | 0.82 | 2320 | 0.5342 | | 0.3503 | 0.83 | 2340 | 0.5348 | | 0.4874 | 0.84 | 2360 | 0.5313 | | 0.5999 | 0.84 | 2380 | 0.5312 | | 0.2757 | 0.85 | 2400 | 0.5292 | | 0.322 | 0.86 | 2420 | 0.5299 | | 0.4368 | 0.86 | 2440 | 0.5318 | | 0.318 | 0.87 | 2460 | 0.5319 | | 0.7192 | 0.88 | 2480 | 0.5307 | | 0.3465 | 0.88 | 2500 | 0.5315 | | 0.4098 | 0.89 | 2520 | 0.5312 | | 0.682 | 0.9 | 2540 | 0.5308 | | 1.4551 | 0.91 | 2560 | 0.5310 | | 0.4396 | 0.91 | 2580 | 0.5301 | | 0.5932 | 0.92 | 2600 | 0.5298 | | 0.3978 | 0.93 | 2620 | 0.5292 | | 0.2823 | 0.93 | 2640 | 0.5296 | | 0.4293 | 0.94 | 2660 | 0.5294 | | 0.4646 | 0.95 | 2680 | 0.5296 | | 0.8203 | 0.96 | 2700 | 0.5295 | | 0.351 | 0.96 | 2720 | 0.5299 | | 0.328 | 0.97 | 2740 | 0.5292 | | 0.3347 | 0.98 | 2760 | 0.5302 | | 0.2608 | 0.98 | 2780 | 0.5296 | | 0.4274 | 0.99 | 2800 | 0.5294 | | 0.3349 | 1.0 | 2820 | 0.5302 | ### Framework versions - Transformers 4.34.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1