File size: 3,265 Bytes
df50375 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 |
---
base_model: meta-llama/Llama-2-13b-hf
tags:
- generated_from_trainer
model-index:
- name: qlora-out
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. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
# qlora-out
This model is a fine-tuned version of [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5407
## 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: 0.0004
- train_batch_size: 2
- eval_batch_size: 2
- 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: cosine
- lr_scheduler_warmup_steps: 300
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8973 | 0.03 | 20 | 0.7029 |
| 0.6828 | 0.06 | 40 | 0.6521 |
| 0.6521 | 0.09 | 60 | 0.6199 |
| 0.7857 | 0.11 | 80 | 0.6066 |
| 0.6208 | 0.14 | 100 | 0.6063 |
| 0.6805 | 0.17 | 120 | 0.5969 |
| 0.5928 | 0.2 | 140 | 0.5989 |
| 0.715 | 0.23 | 160 | 0.5844 |
| 0.5647 | 0.26 | 180 | 0.5979 |
| 0.6778 | 0.29 | 200 | 0.5889 |
| 0.5907 | 0.31 | 220 | 0.5772 |
| 0.5536 | 0.34 | 240 | 0.5917 |
| 0.7422 | 0.37 | 260 | 0.6781 |
| 0.6328 | 0.4 | 280 | 0.5785 |
| 0.5705 | 0.43 | 300 | 0.5720 |
| 0.6124 | 0.46 | 320 | 0.5753 |
| 0.4735 | 0.49 | 340 | 0.6203 |
| 0.4602 | 0.52 | 360 | 0.5772 |
| 0.8571 | 0.54 | 380 | 0.5750 |
| 0.5504 | 0.57 | 400 | 0.6040 |
| 0.6307 | 0.6 | 420 | 0.5796 |
| 0.4782 | 0.63 | 440 | 0.5639 |
| 0.4159 | 0.66 | 460 | 0.5689 |
| 0.6393 | 0.69 | 480 | 0.5661 |
| 0.8243 | 0.72 | 500 | 0.5698 |
| 0.4744 | 0.74 | 520 | 0.5536 |
| 0.4395 | 0.77 | 540 | 0.5536 |
| 0.543 | 0.8 | 560 | 0.5493 |
| 0.4451 | 0.83 | 580 | 0.5421 |
| 0.5384 | 0.86 | 600 | 0.5467 |
| 0.4438 | 0.89 | 620 | 0.5379 |
| 0.4168 | 0.92 | 640 | 0.5398 |
| 0.469 | 0.94 | 660 | 0.5402 |
| 0.6766 | 0.97 | 680 | 0.5407 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|