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--- |
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license: mit |
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library_name: peft |
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tags: |
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- trl |
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- sft |
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- generated_from_trainer |
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datasets: |
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- generator |
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base_model: microsoft/Phi-3-mini-4k-instruct |
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model-index: |
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- name: phi-ft-1000000-fp-newsplit |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# phi-ft-1000000-fp-newsplit |
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This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on the generator dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.7754 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 0 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.2 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 3.1002 | 0.0114 | 100 | 3.0505 | |
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| 2.1929 | 0.0229 | 200 | 2.0493 | |
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| 1.6369 | 0.0343 | 300 | 1.6432 | |
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| 1.4618 | 0.0458 | 400 | 1.5580 | |
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| 1.317 | 0.0572 | 500 | 1.5410 | |
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| 1.1329 | 0.0687 | 600 | 1.6269 | |
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| 0.9505 | 0.0801 | 700 | 1.7387 | |
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| 0.8334 | 0.0916 | 800 | 1.7443 | |
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| 0.7692 | 0.1030 | 900 | 1.7634 | |
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| 0.6983 | 0.1145 | 1000 | 1.7546 | |
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| 0.6859 | 0.1259 | 1100 | 1.7593 | |
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| 0.6671 | 0.1374 | 1200 | 1.7647 | |
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| 0.6285 | 0.1488 | 1300 | 1.7951 | |
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| 0.6121 | 0.1603 | 1400 | 1.7816 | |
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| 0.5923 | 0.1717 | 1500 | 1.8132 | |
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| 0.5908 | 0.1832 | 1600 | 1.7664 | |
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| 0.5662 | 0.1946 | 1700 | 1.8307 | |
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| 0.5637 | 0.2060 | 1800 | 1.7864 | |
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| 0.5475 | 0.2175 | 1900 | 1.7988 | |
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| 0.5421 | 0.2289 | 2000 | 1.7876 | |
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| 0.529 | 0.2404 | 2100 | 1.7661 | |
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| 0.5202 | 0.2518 | 2200 | 1.7709 | |
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| 0.5287 | 0.2633 | 2300 | 1.7681 | |
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| 0.514 | 0.2747 | 2400 | 1.7765 | |
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| 0.5026 | 0.2862 | 2500 | 1.7931 | |
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| 0.5038 | 0.2976 | 2600 | 1.7808 | |
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| 0.5052 | 0.3091 | 2700 | 1.7689 | |
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| 0.4918 | 0.3205 | 2800 | 1.7862 | |
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| 0.4817 | 0.3320 | 2900 | 1.7916 | |
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| 0.4806 | 0.3434 | 3000 | 1.7796 | |
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| 0.4849 | 0.3549 | 3100 | 1.7654 | |
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| 0.4784 | 0.3663 | 3200 | 1.7576 | |
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| 0.4712 | 0.3777 | 3300 | 1.7746 | |
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| 0.4715 | 0.3892 | 3400 | 1.7568 | |
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| 0.4608 | 0.4006 | 3500 | 1.7424 | |
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| 0.4629 | 0.4121 | 3600 | 1.7561 | |
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| 0.4591 | 0.4235 | 3700 | 1.7498 | |
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| 0.4652 | 0.4350 | 3800 | 1.7366 | |
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| 0.461 | 0.4464 | 3900 | 1.7394 | |
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| 0.4469 | 0.4579 | 4000 | 1.7397 | |
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| 0.4521 | 0.4693 | 4100 | 1.7555 | |
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| 0.4498 | 0.4808 | 4200 | 1.7652 | |
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| 0.4541 | 0.4922 | 4300 | 1.7583 | |
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| 0.4594 | 0.5037 | 4400 | 1.7605 | |
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| 0.4514 | 0.5151 | 4500 | 1.7686 | |
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| 0.4395 | 0.5266 | 4600 | 1.7714 | |
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| 0.4384 | 0.5380 | 4700 | 1.7889 | |
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| 0.4392 | 0.5495 | 4800 | 1.7709 | |
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| 0.4495 | 0.5609 | 4900 | 1.7554 | |
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| 0.4375 | 0.5723 | 5000 | 1.7532 | |
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| 0.4441 | 0.5838 | 5100 | 1.7770 | |
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| 0.4458 | 0.5952 | 5200 | 1.7528 | |
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| 0.4343 | 0.6067 | 5300 | 1.7646 | |
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| 0.433 | 0.6181 | 5400 | 1.7689 | |
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| 0.4371 | 0.6296 | 5500 | 1.7738 | |
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| 0.4376 | 0.6410 | 5600 | 1.7633 | |
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| 0.4366 | 0.6525 | 5700 | 1.7810 | |
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| 0.43 | 0.6639 | 5800 | 1.7685 | |
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| 0.4345 | 0.6754 | 5900 | 1.7761 | |
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| 0.4379 | 0.6868 | 6000 | 1.7782 | |
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| 0.4294 | 0.6983 | 6100 | 1.7737 | |
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| 0.4441 | 0.7097 | 6200 | 1.7646 | |
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| 0.4396 | 0.7212 | 6300 | 1.7779 | |
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| 0.4307 | 0.7326 | 6400 | 1.7766 | |
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| 0.4331 | 0.7440 | 6500 | 1.7733 | |
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| 0.4326 | 0.7555 | 6600 | 1.7796 | |
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| 0.4286 | 0.7669 | 6700 | 1.7803 | |
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| 0.4294 | 0.7784 | 6800 | 1.7787 | |
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| 0.4294 | 0.7898 | 6900 | 1.7795 | |
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| 0.4364 | 0.8013 | 7000 | 1.7765 | |
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| 0.4414 | 0.8127 | 7100 | 1.7783 | |
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| 0.4336 | 0.8242 | 7200 | 1.7746 | |
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| 0.4324 | 0.8356 | 7300 | 1.7728 | |
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| 0.4414 | 0.8471 | 7400 | 1.7765 | |
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| 0.4288 | 0.8585 | 7500 | 1.7792 | |
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| 0.4359 | 0.8700 | 7600 | 1.7776 | |
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| 0.4242 | 0.8814 | 7700 | 1.7762 | |
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| 0.4413 | 0.8929 | 7800 | 1.7751 | |
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| 0.4402 | 0.9043 | 7900 | 1.7754 | |
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| 0.4452 | 0.9158 | 8000 | 1.7750 | |
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| 0.4346 | 0.9272 | 8100 | 1.7755 | |
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| 0.4396 | 0.9386 | 8200 | 1.7751 | |
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| 0.44 | 0.9501 | 8300 | 1.7752 | |
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| 0.4333 | 0.9615 | 8400 | 1.7753 | |
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| 0.4348 | 0.9730 | 8500 | 1.7754 | |
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| 0.4331 | 0.9844 | 8600 | 1.7752 | |
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| 0.4326 | 0.9959 | 8700 | 1.7754 | |
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### Framework versions |
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- PEFT 0.10.0 |
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- Transformers 4.40.0 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.16.0 |
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- Tokenizers 0.19.1 |
