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