---
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