Bloom-Medical-QA-LoRA
This model is a fine-tuned version of bigscience/bloom-560m on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.9682
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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 0.0168 | 10 | 2.5135 |
| No log | 0.0337 | 20 | 2.3298 |
| No log | 0.0505 | 30 | 2.2756 |
| No log | 0.0673 | 40 | 2.2419 |
| No log | 0.0842 | 50 | 2.2270 |
| No log | 0.1010 | 60 | 2.2102 |
| No log | 0.1178 | 70 | 2.1931 |
| No log | 0.1347 | 80 | 2.1846 |
| No log | 0.1515 | 90 | 2.1749 |
| 2.2471 | 0.1684 | 100 | 2.1645 |
| 2.2471 | 0.1852 | 110 | 2.1594 |
| 2.2471 | 0.2020 | 120 | 2.1557 |
| 2.2471 | 0.2189 | 130 | 2.1526 |
| 2.2471 | 0.2357 | 140 | 2.1495 |
| 2.2471 | 0.2525 | 150 | 2.1498 |
| 2.2471 | 0.2694 | 160 | 2.1401 |
| 2.2471 | 0.2862 | 170 | 2.1374 |
| 2.2471 | 0.3030 | 180 | 2.1317 |
| 2.2471 | 0.3199 | 190 | 2.1259 |
| 2.1174 | 0.3367 | 200 | 2.1248 |
| 2.1174 | 0.3535 | 210 | 2.1240 |
| 2.1174 | 0.3704 | 220 | 2.1179 |
| 2.1174 | 0.3872 | 230 | 2.1162 |
| 2.1174 | 0.4040 | 240 | 2.1163 |
| 2.1174 | 0.4209 | 250 | 2.1079 |
| 2.1174 | 0.4377 | 260 | 2.1075 |
| 2.1174 | 0.4545 | 270 | 2.1035 |
| 2.1174 | 0.4714 | 280 | 2.1030 |
| 2.1174 | 0.4882 | 290 | 2.0973 |
| 2.0744 | 0.5051 | 300 | 2.0895 |
| 2.0744 | 0.5219 | 310 | 2.0886 |
| 2.0744 | 0.5387 | 320 | 2.0894 |
| 2.0744 | 0.5556 | 330 | 2.0908 |
| 2.0744 | 0.5724 | 340 | 2.0841 |
| 2.0744 | 0.5892 | 350 | 2.0799 |
| 2.0744 | 0.6061 | 360 | 2.0799 |
| 2.0744 | 0.6229 | 370 | 2.0755 |
| 2.0744 | 0.6397 | 380 | 2.0753 |
| 2.0744 | 0.6566 | 390 | 2.0713 |
| 2.054 | 0.6734 | 400 | 2.0713 |
| 2.054 | 0.6902 | 410 | 2.0704 |
| 2.054 | 0.7071 | 420 | 2.0678 |
| 2.054 | 0.7239 | 430 | 2.0664 |
| 2.054 | 0.7407 | 440 | 2.0700 |
| 2.054 | 0.7576 | 450 | 2.0672 |
| 2.054 | 0.7744 | 460 | 2.0639 |
| 2.054 | 0.7912 | 470 | 2.0624 |
| 2.054 | 0.8081 | 480 | 2.0630 |
| 2.054 | 0.8249 | 490 | 2.0579 |
| 2.0228 | 0.8418 | 500 | 2.0548 |
| 2.0228 | 0.8586 | 510 | 2.0517 |
| 2.0228 | 0.8754 | 520 | 2.0490 |
| 2.0228 | 0.8923 | 530 | 2.0509 |
| 2.0228 | 0.9091 | 540 | 2.0488 |
| 2.0228 | 0.9259 | 550 | 2.0482 |
| 2.0228 | 0.9428 | 560 | 2.0446 |
| 2.0228 | 0.9596 | 570 | 2.0407 |
| 2.0228 | 0.9764 | 580 | 2.0445 |
| 2.0228 | 0.9933 | 590 | 2.0410 |
| 2.0186 | 1.0101 | 600 | 2.0426 |
| 2.0186 | 1.0269 | 610 | 2.0408 |
| 2.0186 | 1.0438 | 620 | 2.0407 |
| 2.0186 | 1.0606 | 630 | 2.0404 |
| 2.0186 | 1.0774 | 640 | 2.0363 |
| 2.0186 | 1.0943 | 650 | 2.0358 |
| 2.0186 | 1.1111 | 660 | 2.0365 |
| 2.0186 | 1.1279 | 670 | 2.0348 |
| 2.0186 | 1.1448 | 680 | 2.0343 |
| 2.0186 | 1.1616 | 690 | 2.0319 |
| 1.9341 | 1.1785 | 700 | 2.0268 |
| 1.9341 | 1.1953 | 710 | 2.0287 |
| 1.9341 | 1.2121 | 720 | 2.0302 |
| 1.9341 | 1.2290 | 730 | 2.0298 |
| 1.9341 | 1.2458 | 740 | 2.0307 |
| 1.9341 | 1.2626 | 750 | 2.0282 |
| 1.9341 | 1.2795 | 760 | 2.0305 |
| 1.9341 | 1.2963 | 770 | 2.0240 |
| 1.9341 | 1.3131 | 780 | 2.0245 |
| 1.9341 | 1.3300 | 790 | 2.0204 |
| 1.9309 | 1.3468 | 800 | 2.0208 |
| 1.9309 | 1.3636 | 810 | 2.0189 |
| 1.9309 | 1.3805 | 820 | 2.0179 |
| 1.9309 | 1.3973 | 830 | 2.0185 |
| 1.9309 | 1.4141 | 840 | 2.0187 |
| 1.9309 | 1.4310 | 850 | 2.0191 |
| 1.9309 | 1.4478 | 860 | 2.0218 |
| 1.9309 | 1.4646 | 870 | 2.0131 |
| 1.9309 | 1.4815 | 880 | 2.0096 |
| 1.9309 | 1.4983 | 890 | 2.0092 |
| 1.919 | 1.5152 | 900 | 2.0070 |
| 1.919 | 1.5320 | 910 | 2.0060 |
| 1.919 | 1.5488 | 920 | 2.0070 |
| 1.919 | 1.5657 | 930 | 2.0094 |
| 1.919 | 1.5825 | 940 | 2.0137 |
| 1.919 | 1.5993 | 950 | 2.0092 |
| 1.919 | 1.6162 | 960 | 2.0033 |
| 1.919 | 1.6330 | 970 | 2.0045 |
| 1.919 | 1.6498 | 980 | 2.0053 |
| 1.919 | 1.6667 | 990 | 2.0043 |
| 1.9546 | 1.6835 | 1000 | 2.0028 |
| 1.9546 | 1.7003 | 1010 | 2.0018 |
| 1.9546 | 1.7172 | 1020 | 2.0005 |
| 1.9546 | 1.7340 | 1030 | 1.9999 |
| 1.9546 | 1.7508 | 1040 | 1.9983 |
| 1.9546 | 1.7677 | 1050 | 1.9974 |
| 1.9546 | 1.7845 | 1060 | 1.9971 |
| 1.9546 | 1.8013 | 1070 | 1.9961 |
| 1.9546 | 1.8182 | 1080 | 1.9943 |
| 1.9546 | 1.8350 | 1090 | 1.9938 |
| 1.9247 | 1.8519 | 1100 | 1.9949 |
| 1.9247 | 1.8687 | 1110 | 1.9949 |
| 1.9247 | 1.8855 | 1120 | 1.9926 |
| 1.9247 | 1.9024 | 1130 | 1.9888 |
| 1.9247 | 1.9192 | 1140 | 1.9894 |
| 1.9247 | 1.9360 | 1150 | 1.9893 |
| 1.9247 | 1.9529 | 1160 | 1.9912 |
| 1.9247 | 1.9697 | 1170 | 1.9896 |
| 1.9247 | 1.9865 | 1180 | 1.9896 |
| 1.9247 | 2.0034 | 1190 | 1.9900 |
| 1.8978 | 2.0202 | 1200 | 1.9886 |
| 1.8978 | 2.0370 | 1210 | 1.9898 |
| 1.8978 | 2.0539 | 1220 | 1.9883 |
| 1.8978 | 2.0707 | 1230 | 1.9869 |
| 1.8978 | 2.0875 | 1240 | 1.9876 |
| 1.8978 | 2.1044 | 1250 | 1.9873 |
| 1.8978 | 2.1212 | 1260 | 1.9893 |
| 1.8978 | 2.1380 | 1270 | 1.9879 |
| 1.8978 | 2.1549 | 1280 | 1.9880 |
| 1.8978 | 2.1717 | 1290 | 1.9882 |
| 1.8637 | 2.1886 | 1300 | 1.9869 |
| 1.8637 | 2.2054 | 1310 | 1.9879 |
| 1.8637 | 2.2222 | 1320 | 1.9881 |
| 1.8637 | 2.2391 | 1330 | 1.9901 |
| 1.8637 | 2.2559 | 1340 | 1.9874 |
| 1.8637 | 2.2727 | 1350 | 1.9855 |
| 1.8637 | 2.2896 | 1360 | 1.9871 |
| 1.8637 | 2.3064 | 1370 | 1.9871 |
| 1.8637 | 2.3232 | 1380 | 1.9849 |
| 1.8637 | 2.3401 | 1390 | 1.9837 |
| 1.8206 | 2.3569 | 1400 | 1.9841 |
| 1.8206 | 2.3737 | 1410 | 1.9828 |
| 1.8206 | 2.3906 | 1420 | 1.9809 |
| 1.8206 | 2.4074 | 1430 | 1.9774 |
| 1.8206 | 2.4242 | 1440 | 1.9770 |
| 1.8206 | 2.4411 | 1450 | 1.9779 |
| 1.8206 | 2.4579 | 1460 | 1.9779 |
| 1.8206 | 2.4747 | 1470 | 1.9783 |
| 1.8206 | 2.4916 | 1480 | 1.9763 |
| 1.8206 | 2.5084 | 1490 | 1.9766 |
| 1.8587 | 2.5253 | 1500 | 1.9762 |
| 1.8587 | 2.5421 | 1510 | 1.9768 |
| 1.8587 | 2.5589 | 1520 | 1.9782 |
| 1.8587 | 2.5758 | 1530 | 1.9787 |
| 1.8587 | 2.5926 | 1540 | 1.9770 |
| 1.8587 | 2.6094 | 1550 | 1.9755 |
| 1.8587 | 2.6263 | 1560 | 1.9753 |
| 1.8587 | 2.6431 | 1570 | 1.9753 |
| 1.8587 | 2.6599 | 1580 | 1.9747 |
| 1.8587 | 2.6768 | 1590 | 1.9742 |
| 1.8226 | 2.6936 | 1600 | 1.9743 |
| 1.8226 | 2.7104 | 1610 | 1.9729 |
| 1.8226 | 2.7273 | 1620 | 1.9735 |
| 1.8226 | 2.7441 | 1630 | 1.9749 |
| 1.8226 | 2.7609 | 1640 | 1.9737 |
| 1.8226 | 2.7778 | 1650 | 1.9731 |
| 1.8226 | 2.7946 | 1660 | 1.9723 |
| 1.8226 | 2.8114 | 1670 | 1.9721 |
| 1.8226 | 2.8283 | 1680 | 1.9713 |
| 1.8226 | 2.8451 | 1690 | 1.9698 |
| 1.8331 | 2.8620 | 1700 | 1.9695 |
| 1.8331 | 2.8788 | 1710 | 1.9688 |
| 1.8331 | 2.8956 | 1720 | 1.9689 |
| 1.8331 | 2.9125 | 1730 | 1.9686 |
| 1.8331 | 2.9293 | 1740 | 1.9685 |
| 1.8331 | 2.9461 | 1750 | 1.9681 |
| 1.8331 | 2.9630 | 1760 | 1.9675 |
| 1.8331 | 2.9798 | 1770 | 1.9669 |
| 1.8331 | 2.9966 | 1780 | 1.9671 |
| 1.8331 | 3.0135 | 1790 | 1.9680 |
| 1.8251 | 3.0303 | 1800 | 1.9696 |
| 1.8251 | 3.0471 | 1810 | 1.9705 |
| 1.8251 | 3.0640 | 1820 | 1.9695 |
| 1.8251 | 3.0808 | 1830 | 1.9700 |
| 1.8251 | 3.0976 | 1840 | 1.9701 |
| 1.8251 | 3.1145 | 1850 | 1.9701 |
| 1.8251 | 3.1313 | 1860 | 1.9705 |
| 1.8251 | 3.1481 | 1870 | 1.9707 |
| 1.8251 | 3.1650 | 1880 | 1.9709 |
| 1.8251 | 3.1818 | 1890 | 1.9712 |
| 1.7809 | 3.1987 | 1900 | 1.9713 |
| 1.7809 | 3.2155 | 1910 | 1.9718 |
| 1.7809 | 3.2323 | 1920 | 1.9720 |
| 1.7809 | 3.2492 | 1930 | 1.9714 |
| 1.7809 | 3.2660 | 1940 | 1.9704 |
| 1.7809 | 3.2828 | 1950 | 1.9694 |
| 1.7809 | 3.2997 | 1960 | 1.9694 |
| 1.7809 | 3.3165 | 1970 | 1.9692 |
| 1.7809 | 3.3333 | 1980 | 1.9690 |
| 1.7809 | 3.3502 | 1990 | 1.9689 |
| 1.7957 | 3.3670 | 2000 | 1.9687 |
| 1.7957 | 3.3838 | 2010 | 1.9685 |
| 1.7957 | 3.4007 | 2020 | 1.9682 |
| 1.7957 | 3.4175 | 2030 | 1.9684 |
| 1.7957 | 3.4343 | 2040 | 1.9688 |
| 1.7957 | 3.4512 | 2050 | 1.9689 |
| 1.7957 | 3.4680 | 2060 | 1.9685 |
| 1.7957 | 3.4848 | 2070 | 1.9683 |
| 1.7957 | 3.5017 | 2080 | 1.9685 |
| 1.7957 | 3.5185 | 2090 | 1.9686 |
| 1.7859 | 3.5354 | 2100 | 1.9688 |
| 1.7859 | 3.5522 | 2110 | 1.9691 |
| 1.7859 | 3.5690 | 2120 | 1.9692 |
| 1.7859 | 3.5859 | 2130 | 1.9693 |
| 1.7859 | 3.6027 | 2140 | 1.9694 |
| 1.7859 | 3.6195 | 2150 | 1.9692 |
| 1.7859 | 3.6364 | 2160 | 1.9690 |
| 1.7859 | 3.6532 | 2170 | 1.9689 |
| 1.7859 | 3.6700 | 2180 | 1.9689 |
| 1.7859 | 3.6869 | 2190 | 1.9689 |
| 1.7794 | 3.7037 | 2200 | 1.9687 |
| 1.7794 | 3.7205 | 2210 | 1.9686 |
| 1.7794 | 3.7374 | 2220 | 1.9685 |
| 1.7794 | 3.7542 | 2230 | 1.9685 |
| 1.7794 | 3.7710 | 2240 | 1.9684 |
| 1.7794 | 3.7879 | 2250 | 1.9684 |
| 1.7794 | 3.8047 | 2260 | 1.9683 |
| 1.7794 | 3.8215 | 2270 | 1.9683 |
| 1.7794 | 3.8384 | 2280 | 1.9682 |
| 1.7794 | 3.8552 | 2290 | 1.9682 |
| 1.7766 | 3.8721 | 2300 | 1.9682 |
| 1.7766 | 3.8889 | 2310 | 1.9682 |
| 1.7766 | 3.9057 | 2320 | 1.9682 |
| 1.7766 | 3.9226 | 2330 | 1.9682 |
| 1.7766 | 3.9394 | 2340 | 1.9682 |
| 1.7766 | 3.9562 | 2350 | 1.9682 |
| 1.7766 | 3.9731 | 2360 | 1.9682 |
| 1.7766 | 3.9899 | 2370 | 1.9682 |
Framework versions
- PEFT 0.11.1
- Transformers 4.42.4
- Pytorch 1.13.1+cu117
- Datasets 2.19.2
- Tokenizers 0.19.1
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Model tree for aryaadhi/Bloom-Medical-QA-LoRA
Base model
bigscience/bloom-560m