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---
license: mit
library_name: peft
tags:
- generated_from_trainer
base_model: microsoft/phi-2
model-index:
- name: fine-tuning-Phi2-with-webglm-qa-with-lora_4
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. -->
# fine-tuning-Phi2-with-webglm-qa-with-lora_4
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2392
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 10
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 8.19 | 0.2 | 10 | 7.9966 |
| 8.0261 | 0.4 | 20 | 7.7896 |
| 7.3527 | 0.6 | 30 | 7.2580 |
| 6.9568 | 0.8 | 40 | 5.9952 |
| 5.2411 | 1.0 | 50 | 3.7880 |
| 2.9772 | 1.2 | 60 | 1.8751 |
| 1.2384 | 1.39 | 70 | 0.7517 |
| 0.6916 | 1.59 | 80 | 0.6684 |
| 0.5669 | 1.79 | 90 | 0.6138 |
| 0.5195 | 1.99 | 100 | 0.5846 |
| 0.5281 | 2.19 | 110 | 0.5607 |
| 0.4764 | 2.39 | 120 | 0.5396 |
| 0.4655 | 2.59 | 130 | 0.5190 |
| 0.4787 | 2.79 | 140 | 0.4980 |
| 0.427 | 2.99 | 150 | 0.4765 |
| 0.41 | 3.19 | 160 | 0.4547 |
| 0.397 | 3.39 | 170 | 0.4317 |
| 0.3648 | 3.59 | 180 | 0.4087 |
| 0.3436 | 3.78 | 190 | 0.3863 |
| 0.3415 | 3.98 | 200 | 0.3661 |
| 0.3072 | 4.18 | 210 | 0.3481 |
| 0.2681 | 4.38 | 220 | 0.3341 |
| 0.3068 | 4.58 | 230 | 0.3201 |
| 0.2526 | 4.78 | 240 | 0.3095 |
| 0.2632 | 4.98 | 250 | 0.3003 |
| 0.2693 | 5.18 | 260 | 0.2936 |
| 0.2194 | 5.38 | 270 | 0.2874 |
| 0.2474 | 5.58 | 280 | 0.2826 |
| 0.2467 | 5.78 | 290 | 0.2770 |
| 0.2188 | 5.98 | 300 | 0.2726 |
| 0.2305 | 6.18 | 310 | 0.2690 |
| 0.2336 | 6.37 | 320 | 0.2643 |
| 0.2192 | 6.57 | 330 | 0.2614 |
| 0.2189 | 6.77 | 340 | 0.2588 |
| 0.2049 | 6.97 | 350 | 0.2564 |
| 0.2096 | 7.17 | 360 | 0.2540 |
| 0.221 | 7.37 | 370 | 0.2521 |
| 0.2167 | 7.57 | 380 | 0.2498 |
| 0.203 | 7.77 | 390 | 0.2484 |
| 0.1999 | 7.97 | 400 | 0.2469 |
| 0.1888 | 8.17 | 410 | 0.2458 |
| 0.195 | 8.37 | 420 | 0.2443 |
| 0.2358 | 8.57 | 430 | 0.2429 |
| 0.1929 | 8.76 | 440 | 0.2419 |
| 0.2066 | 8.96 | 450 | 0.2412 |
| 0.2101 | 9.16 | 460 | 0.2407 |
| 0.2009 | 9.36 | 470 | 0.2400 |
| 0.1976 | 9.56 | 480 | 0.2394 |
| 0.2013 | 9.76 | 490 | 0.2392 |
| 0.1956 | 9.96 | 500 | 0.2392 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.15.0 |