language:
- nl
license: cc-by-nc-4.0
datasets:
- BramVanroy/alpaca-cleaned-dutch
inference: false
base_model: ybelkada/falcon-7b-sharded-bf16
model-index:
- name: falcon-7b-ft-alpaca-cleaned-dutch
results: []
falcon-7b-ft-alpaca-cleaned-dutch
Model description
This model is a fine-tuned version of ybelkada/falcon-7b-sharded-bf16 on the BramVanroy/alpaca-cleaned-dutch dataset. See the original Falcon 7B model for more information, intended use, and biases.
Intended uses & limitations
This model is intended as a (poor) baseline for Dutch generative LLMs. It by no means aims to provide SOTA performance and is specifically intended for research purposes, and an opportunity for me to test hyperparameters and stability.
Importantly, the original Falcon 7B model was only trained on English and French. Therefore, Dutch generations should be taken with a massive grain of salt.
Training and evaluation data
Trained on the synthetic BramVanroy/alpaca-cleaned-dutch instruction dataset. Therefore, commercial use of this model is forbidden. The model is intended for research purposes only.
Training procedure
Trained with LoRA and merged before upload.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.9832 | 0.03 | 10 | 1.8889 |
| 1.9355 | 0.05 | 20 | 1.8834 |
| 1.9694 | 0.08 | 30 | 1.8671 |
| 1.9048 | 0.1 | 40 | 1.8328 |
| 1.8443 | 0.13 | 50 | 1.7970 |
| 1.7448 | 0.16 | 60 | 1.7711 |
| 1.8004 | 0.18 | 70 | 1.7522 |
| 1.7767 | 0.21 | 80 | 1.7370 |
| 1.7733 | 0.23 | 90 | 1.7248 |
| 1.7926 | 0.26 | 100 | 1.7149 |
| 1.8258 | 0.29 | 110 | 1.7066 |
| 1.6709 | 0.31 | 120 | 1.6993 |
| 1.6612 | 0.34 | 130 | 1.6926 |
| 1.8463 | 0.36 | 140 | 1.6867 |
| 1.8413 | 0.39 | 150 | 1.6814 |
| 1.7659 | 0.42 | 160 | 1.6765 |
| 1.69 | 0.44 | 170 | 1.6715 |
| 1.7219 | 0.47 | 180 | 1.6673 |
| 1.6755 | 0.49 | 190 | 1.6627 |
| 1.7823 | 0.52 | 200 | 1.6584 |
| 1.7635 | 0.55 | 210 | 1.6545 |
| 1.7335 | 0.57 | 220 | 1.6506 |
| 1.7272 | 0.6 | 230 | 1.6471 |
| 1.718 | 0.63 | 240 | 1.6436 |
| 1.6899 | 0.65 | 250 | 1.6403 |
| 1.622 | 0.68 | 260 | 1.6370 |
| 1.6556 | 0.7 | 270 | 1.6337 |
| 1.7912 | 0.73 | 280 | 1.6304 |
| 1.6025 | 0.76 | 290 | 1.6274 |
| 1.7181 | 0.78 | 300 | 1.6246 |
| 1.7452 | 0.81 | 310 | 1.6217 |
| 1.5975 | 0.83 | 320 | 1.6189 |
| 1.5754 | 0.86 | 330 | 1.6162 |
| 1.7077 | 0.89 | 340 | 1.6136 |
| 1.5848 | 0.91 | 350 | 1.6112 |
| 1.7011 | 0.94 | 360 | 1.6087 |
| 1.6697 | 0.96 | 370 | 1.6065 |
| 1.6633 | 0.99 | 380 | 1.6042 |
| 1.6722 | 1.02 | 390 | 1.6015 |
| 1.7181 | 1.04 | 400 | 1.5993 |
| 1.6414 | 1.07 | 410 | 1.5972 |
| 1.6856 | 1.09 | 420 | 1.5952 |
| 1.6491 | 1.12 | 430 | 1.5930 |
| 1.6736 | 1.15 | 440 | 1.5912 |
| 1.619 | 1.17 | 450 | 1.5893 |
| 1.6452 | 1.2 | 460 | 1.5870 |
| 1.6498 | 1.22 | 470 | 1.5854 |
| 1.675 | 1.25 | 480 | 1.5839 |
| 1.684 | 1.28 | 490 | 1.5823 |
| 1.6379 | 1.3 | 500 | 1.5802 |
| 1.5173 | 1.33 | 510 | 1.5786 |
| 1.6443 | 1.35 | 520 | 1.5773 |
| 1.5628 | 1.38 | 530 | 1.5755 |
| 1.7287 | 1.41 | 540 | 1.5738 |
| 1.5615 | 1.43 | 550 | 1.5725 |
| 1.6129 | 1.46 | 560 | 1.5712 |
| 1.6709 | 1.48 | 570 | 1.5700 |
| 1.5818 | 1.51 | 580 | 1.5683 |
| 1.6358 | 1.54 | 590 | 1.5672 |
| 1.6513 | 1.56 | 600 | 1.5662 |
| 1.5637 | 1.59 | 610 | 1.5654 |
| 1.612 | 1.62 | 620 | 1.5643 |
| 1.6396 | 1.64 | 630 | 1.5630 |
| 1.6414 | 1.67 | 640 | 1.5620 |
| 1.6096 | 1.69 | 650 | 1.5611 |
| 1.6149 | 1.72 | 660 | 1.5603 |
| 1.5886 | 1.75 | 670 | 1.5593 |
| 1.537 | 1.77 | 680 | 1.5582 |
| 1.5883 | 1.8 | 690 | 1.5574 |
| 1.6512 | 1.82 | 700 | 1.5566 |
| 1.683 | 1.85 | 710 | 1.5559 |
| 1.7059 | 1.88 | 720 | 1.5549 |
| 1.5453 | 1.9 | 730 | 1.5542 |
| 1.5738 | 1.93 | 740 | 1.5536 |
| 1.6004 | 1.95 | 750 | 1.5530 |
| 1.6753 | 1.98 | 760 | 1.5523 |
| 1.6362 | 2.01 | 770 | 1.5517 |
| 1.5805 | 2.03 | 780 | 1.5511 |
| 1.6416 | 2.06 | 790 | 1.5508 |
| 1.5755 | 2.08 | 800 | 1.5506 |
| 1.5763 | 2.11 | 810 | 1.5501 |
| 1.7112 | 2.14 | 820 | 1.5497 |
| 1.6533 | 2.16 | 830 | 1.5493 |
| 1.6008 | 2.19 | 840 | 1.5489 |
| 1.5731 | 2.21 | 850 | 1.5485 |
| 1.4975 | 2.24 | 860 | 1.5480 |
| 1.6158 | 2.27 | 870 | 1.5478 |
| 1.6063 | 2.29 | 880 | 1.5474 |
| 1.628 | 2.32 | 890 | 1.5470 |
| 1.6177 | 2.34 | 900 | 1.5468 |
| 1.5646 | 2.37 | 910 | 1.5467 |
| 1.5272 | 2.4 | 920 | 1.5466 |
| 1.5402 | 2.42 | 930 | 1.5464 |
| 1.5815 | 2.45 | 940 | 1.5461 |
| 1.4857 | 2.47 | 950 | 1.5459 |
| 1.5923 | 2.5 | 960 | 1.5458 |
| 1.6167 | 2.53 | 970 | 1.5456 |
| 1.7214 | 2.55 | 980 | 1.5456 |
| 1.5467 | 2.58 | 990 | 1.5455 |
| 1.6455 | 2.61 | 1000 | 1.5453 |
| 1.6137 | 2.63 | 1010 | 1.5453 |
| 1.6104 | 2.66 | 1020 | 1.5453 |
| 1.6756 | 2.68 | 1030 | 1.5451 |
| 1.5818 | 2.71 | 1040 | 1.5450 |
| 1.5829 | 2.74 | 1050 | 1.5450 |
| 1.5753 | 2.76 | 1060 | 1.5450 |
| 1.6484 | 2.79 | 1070 | 1.5450 |
| 1.6765 | 2.81 | 1080 | 1.5450 |
| 1.623 | 2.84 | 1090 | 1.5449 |
| 1.6901 | 2.87 | 1100 | 1.5449 |
| 1.6601 | 2.89 | 1110 | 1.5449 |
| 1.6763 | 2.92 | 1120 | 1.5449 |
| 1.6203 | 2.94 | 1130 | 1.5449 |
| 1.5113 | 2.97 | 1140 | 1.5448 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3