metadata
base_model: microsoft/Phi-3-mini-4k-instruct
library_name: peft
license: mit
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: phi-3-mini-LoRA
results: []
phi-3-mini-LoRA
This model is a fine-tuned version of microsoft/Phi-3-mini-4k-instruct on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.8588
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: 0.0001
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.0606 | 0.1071 | 100 | 1.0032 |
| 0.9107 | 0.2141 | 200 | 0.9256 |
| 0.8783 | 0.3212 | 300 | 0.9081 |
| 0.8761 | 0.4283 | 400 | 0.8986 |
| 0.8651 | 0.5353 | 500 | 0.8920 |
| 0.864 | 0.6424 | 600 | 0.8875 |
| 0.8759 | 0.7495 | 700 | 0.8828 |
| 0.8584 | 0.8565 | 800 | 0.8807 |
| 0.8677 | 0.9636 | 900 | 0.8784 |
| 0.8507 | 1.0707 | 1000 | 0.8757 |
| 0.8499 | 1.1777 | 1100 | 0.8739 |
| 0.8446 | 1.2848 | 1200 | 0.8718 |
| 0.8637 | 1.3919 | 1300 | 0.8712 |
| 0.8238 | 1.4989 | 1400 | 0.8686 |
| 0.8231 | 1.6060 | 1500 | 0.8681 |
| 0.8361 | 1.7131 | 1600 | 0.8661 |
| 0.8319 | 1.8201 | 1700 | 0.8652 |
| 0.8166 | 1.9272 | 1800 | 0.8643 |
| 0.8312 | 2.0343 | 1900 | 0.8634 |
| 0.834 | 2.1413 | 2000 | 0.8625 |
| 0.8362 | 2.2484 | 2100 | 0.8616 |
| 0.8413 | 2.3555 | 2200 | 0.8611 |
| 0.8153 | 2.4625 | 2300 | 0.8605 |
| 0.8235 | 2.5696 | 2400 | 0.8607 |
| 0.7958 | 2.6767 | 2500 | 0.8598 |
| 0.8137 | 2.7837 | 2600 | 0.8593 |
| 0.8162 | 2.8908 | 2700 | 0.8591 |
| 0.8317 | 2.9979 | 2800 | 0.8588 |
Framework versions
- PEFT 0.12.0
- Transformers 4.43.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1