|
--- |
|
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: [] |
|
--- |
|
|
|
<!-- 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-3-mini-LoRA |
|
|
|
This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/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 |
