pythia-70m_tatsu-lab_alpaca_farm_sftsd0_policy_pythia-6.9b_gold_offsetbias-8b_noise0.25_rmsd0
This model is a fine-tuned version of RylanSchaeffer/EleutherAI_pythia-70m_tatsu-lab_alpaca_farm_sftseed0 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.7578
- Accuracy: 0.5255
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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 0
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.025
- num_epochs: 5
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
No log | 0 | 0 | 0.8506 | 0.5085 |
0.7856 | 0.0648 | 100 | 0.8417 | 0.5154 |
0.8988 | 0.1296 | 200 | 0.8312 | 0.5035 |
0.8425 | 0.1944 | 300 | 0.8257 | 0.5073 |
0.8449 | 0.2592 | 400 | 0.8066 | 0.5158 |
0.7617 | 0.3239 | 500 | 0.8017 | 0.5224 |
0.8037 | 0.3887 | 600 | 0.7950 | 0.5174 |
0.7883 | 0.4535 | 700 | 0.7927 | 0.5204 |
0.7897 | 0.5183 | 800 | 0.7897 | 0.5204 |
0.7966 | 0.5831 | 900 | 0.7776 | 0.5104 |
0.7762 | 0.6479 | 1000 | 0.7783 | 0.5116 |
0.7904 | 0.7127 | 1100 | 0.7675 | 0.5185 |
0.7542 | 0.7775 | 1200 | 0.7670 | 0.5216 |
0.8068 | 0.8422 | 1300 | 0.7714 | 0.5104 |
0.7943 | 0.9070 | 1400 | 0.7693 | 0.5166 |
0.7704 | 0.9718 | 1500 | 0.7697 | 0.5096 |
0.7728 | 1.0366 | 1600 | 0.7666 | 0.5123 |
0.7862 | 1.1014 | 1700 | 0.7671 | 0.5116 |
0.7159 | 1.1662 | 1800 | 0.7709 | 0.5158 |
0.7586 | 1.2310 | 1900 | 0.7631 | 0.5158 |
0.7751 | 1.2958 | 2000 | 0.7678 | 0.5096 |
0.7356 | 1.3605 | 2100 | 0.7665 | 0.5120 |
0.7916 | 1.4253 | 2200 | 0.7610 | 0.5093 |
0.7289 | 1.4901 | 2300 | 0.7586 | 0.5104 |
0.7283 | 1.5549 | 2400 | 0.7570 | 0.5189 |
0.7382 | 1.6197 | 2500 | 0.7611 | 0.5123 |
0.7677 | 1.6845 | 2600 | 0.7582 | 0.5220 |
0.7639 | 1.7493 | 2700 | 0.7587 | 0.5147 |
0.8038 | 1.8141 | 2800 | 0.7589 | 0.5073 |
0.7765 | 1.8788 | 2900 | 0.7631 | 0.5104 |
0.7728 | 1.9436 | 3000 | 0.7565 | 0.5108 |
0.774 | 2.0084 | 3100 | 0.7601 | 0.5216 |
0.751 | 2.0732 | 3200 | 0.7588 | 0.5231 |
0.7838 | 2.1380 | 3300 | 0.7633 | 0.5120 |
0.73 | 2.2028 | 3400 | 0.7583 | 0.5174 |
0.7496 | 2.2676 | 3500 | 0.7594 | 0.5212 |
0.7925 | 2.3324 | 3600 | 0.7641 | 0.5143 |
0.7393 | 2.3971 | 3700 | 0.7589 | 0.5127 |
0.7359 | 2.4619 | 3800 | 0.7586 | 0.5154 |
0.767 | 2.5267 | 3900 | 0.7596 | 0.5112 |
0.7707 | 2.5915 | 4000 | 0.7604 | 0.5066 |
0.7245 | 2.6563 | 4100 | 0.7575 | 0.5143 |
0.7138 | 2.7211 | 4200 | 0.7612 | 0.5162 |
0.7398 | 2.7859 | 4300 | 0.7577 | 0.5193 |
0.7363 | 2.8507 | 4400 | 0.7547 | 0.5235 |
0.733 | 2.9155 | 4500 | 0.7592 | 0.5181 |
0.7584 | 2.9802 | 4600 | 0.7562 | 0.5174 |
0.7846 | 3.0450 | 4700 | 0.7575 | 0.5174 |
0.7295 | 3.1098 | 4800 | 0.7576 | 0.5166 |
0.775 | 3.1746 | 4900 | 0.7538 | 0.5216 |
0.7513 | 3.2394 | 5000 | 0.7599 | 0.5154 |
0.7589 | 3.3042 | 5100 | 0.7601 | 0.5189 |
0.7205 | 3.3690 | 5200 | 0.7568 | 0.5174 |
0.7262 | 3.4338 | 5300 | 0.7554 | 0.5181 |
0.7971 | 3.4985 | 5400 | 0.7591 | 0.5201 |
0.7276 | 3.5633 | 5500 | 0.7579 | 0.5220 |
0.7736 | 3.6281 | 5600 | 0.7588 | 0.5135 |
0.8005 | 3.6929 | 5700 | 0.7599 | 0.5243 |
0.7843 | 3.7577 | 5800 | 0.7563 | 0.5147 |
0.7411 | 3.8225 | 5900 | 0.7595 | 0.5177 |
0.7341 | 3.8873 | 6000 | 0.7589 | 0.5197 |
0.7376 | 3.9521 | 6100 | 0.7582 | 0.5197 |
0.7611 | 4.0168 | 6200 | 0.7560 | 0.5212 |
0.7506 | 4.0816 | 6300 | 0.7570 | 0.5243 |
0.7533 | 4.1464 | 6400 | 0.7557 | 0.5177 |
0.7328 | 4.2112 | 6500 | 0.7555 | 0.5135 |
0.7443 | 4.2760 | 6600 | 0.7565 | 0.5185 |
0.7135 | 4.3408 | 6700 | 0.7576 | 0.5177 |
0.8016 | 4.4056 | 6800 | 0.7590 | 0.5100 |
0.7408 | 4.4704 | 6900 | 0.7554 | 0.5143 |
0.7693 | 4.5351 | 7000 | 0.7550 | 0.5216 |
0.7257 | 4.5999 | 7100 | 0.7569 | 0.5147 |
0.7576 | 4.6647 | 7200 | 0.7586 | 0.5185 |
0.7378 | 4.7295 | 7300 | 0.7560 | 0.5224 |
0.7859 | 4.7943 | 7400 | 0.7566 | 0.5139 |
0.7404 | 4.8591 | 7500 | 0.7546 | 0.5193 |
0.7355 | 4.9239 | 7600 | 0.7572 | 0.5228 |
0.7672 | 4.9887 | 7700 | 0.7575 | 0.5270 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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