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--- |
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base_model: meta-llama/Llama-2-7b-hf |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: qlora-out |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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[<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) |
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# qlora-out |
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This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6420 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 10 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.9758 | 0.03 | 20 | 0.6870 | |
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| 0.7228 | 0.06 | 40 | 0.6791 | |
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| 0.6804 | 0.09 | 60 | 0.6613 | |
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| 0.8117 | 0.11 | 80 | 0.6360 | |
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| 0.6458 | 0.14 | 100 | 0.6335 | |
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| 0.7509 | 0.17 | 120 | 0.6245 | |
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| 0.6174 | 0.2 | 140 | 0.6313 | |
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| 0.7549 | 0.23 | 160 | 0.6180 | |
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| 0.6015 | 0.26 | 180 | 0.6167 | |
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| 0.716 | 0.29 | 200 | 0.6165 | |
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| 0.6304 | 0.31 | 220 | 0.6014 | |
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| 0.5781 | 0.34 | 240 | 0.6107 | |
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| 0.8 | 0.37 | 260 | 0.5949 | |
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| 0.6845 | 0.4 | 280 | 0.5953 | |
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| 0.5857 | 0.43 | 300 | 0.5940 | |
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| 0.6369 | 0.46 | 320 | 0.5889 | |
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| 0.4767 | 0.49 | 340 | 0.5946 | |
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| 0.4848 | 0.52 | 360 | 0.5991 | |
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| 0.9067 | 0.54 | 380 | 0.5943 | |
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| 0.5943 | 0.57 | 400 | 0.5854 | |
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| 0.6999 | 0.6 | 420 | 0.5941 | |
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| 0.5173 | 0.63 | 440 | 0.5887 | |
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| 0.4201 | 0.66 | 460 | 0.5952 | |
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| 0.667 | 0.69 | 480 | 0.5802 | |
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| 0.8568 | 0.72 | 500 | 0.5922 | |
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| 0.515 | 0.74 | 520 | 0.5800 | |
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| 0.504 | 0.77 | 540 | 0.5894 | |
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| 0.6361 | 0.8 | 560 | 0.5983 | |
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| 0.4896 | 0.83 | 580 | 0.5770 | |
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| 0.6044 | 0.86 | 600 | 0.5717 | |
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| 0.4925 | 0.89 | 620 | 0.5715 | |
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| 0.4704 | 0.92 | 640 | 0.5707 | |
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| 0.5342 | 0.94 | 660 | 0.5748 | |
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| 0.755 | 0.97 | 680 | 0.5673 | |
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| 0.6547 | 1.0 | 700 | 0.5721 | |
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| 0.6014 | 1.03 | 720 | 0.5892 | |
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| 0.4692 | 1.06 | 740 | 0.5981 | |
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| 0.407 | 1.09 | 760 | 0.5995 | |
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| 0.5351 | 1.12 | 780 | 0.5948 | |
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| 0.3004 | 1.14 | 800 | 0.5758 | |
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| 0.554 | 1.17 | 820 | 0.5862 | |
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| 0.6394 | 1.2 | 840 | 0.5850 | |
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| 0.7135 | 1.23 | 860 | 0.5900 | |
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| 0.6323 | 1.26 | 880 | 0.5931 | |
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| 0.3257 | 1.29 | 900 | 0.5902 | |
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| 0.5183 | 1.32 | 920 | 0.5763 | |
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| 0.5383 | 1.34 | 940 | 0.5842 | |
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| 0.453 | 1.37 | 960 | 0.5878 | |
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| 0.5305 | 1.4 | 980 | 0.5975 | |
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| 0.4316 | 1.43 | 1000 | 0.5829 | |
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| 0.5992 | 1.46 | 1020 | 0.5801 | |
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| 0.5043 | 1.49 | 1040 | 0.5731 | |
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| 0.4566 | 1.52 | 1060 | 0.5777 | |
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| 0.4879 | 1.55 | 1080 | 0.5785 | |
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| 0.7149 | 1.57 | 1100 | 0.5727 | |
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| 0.4555 | 1.6 | 1120 | 0.5824 | |
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| 0.5248 | 1.63 | 1140 | 0.5821 | |
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| 0.4981 | 1.66 | 1160 | 0.5711 | |
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| 0.5595 | 1.69 | 1180 | 0.5931 | |
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| 0.577 | 1.72 | 1200 | 0.5898 | |
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| 0.3202 | 1.75 | 1220 | 0.5775 | |
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| 0.7182 | 1.77 | 1240 | 0.5800 | |
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| 0.5608 | 1.8 | 1260 | 0.5668 | |
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| 0.5677 | 1.83 | 1280 | 0.5797 | |
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| 0.5046 | 1.86 | 1300 | 0.5725 | |
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| 0.5165 | 1.89 | 1320 | 0.5709 | |
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| 0.6432 | 1.92 | 1340 | 0.5817 | |
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| 0.4973 | 1.95 | 1360 | 0.5695 | |
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| 0.2903 | 1.97 | 1380 | 0.5762 | |
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| 0.3099 | 2.0 | 1400 | 0.5832 | |
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| 0.4383 | 2.03 | 1420 | 0.6773 | |
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| 0.287 | 2.06 | 1440 | 0.6324 | |
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| 0.3395 | 2.09 | 1460 | 0.6600 | |
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| 0.2677 | 2.12 | 1480 | 0.6409 | |
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| 0.4145 | 2.15 | 1500 | 0.6259 | |
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| 0.2435 | 2.17 | 1520 | 0.6528 | |
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| 0.2539 | 2.2 | 1540 | 0.6379 | |
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| 0.3619 | 2.23 | 1560 | 0.6402 | |
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| 0.3289 | 2.26 | 1580 | 0.6355 | |
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| 0.4993 | 2.29 | 1600 | 0.6515 | |
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| 0.2705 | 2.32 | 1620 | 0.6357 | |
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| 0.4863 | 2.35 | 1640 | 0.6385 | |
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| 0.356 | 2.37 | 1660 | 0.6364 | |
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| 0.3433 | 2.4 | 1680 | 0.6390 | |
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| 0.3215 | 2.43 | 1700 | 0.6325 | |
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| 0.4795 | 2.46 | 1720 | 0.6336 | |
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| 0.3457 | 2.49 | 1740 | 0.6342 | |
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| 0.6864 | 2.52 | 1760 | 0.6435 | |
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| 0.3965 | 2.55 | 1780 | 0.6447 | |
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| 0.3424 | 2.58 | 1800 | 0.6344 | |
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| 0.7203 | 2.6 | 1820 | 0.6385 | |
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| 0.6209 | 2.63 | 1840 | 0.6475 | |
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| 0.3693 | 2.66 | 1860 | 0.6439 | |
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| 0.4004 | 2.69 | 1880 | 0.6410 | |
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| 0.3499 | 2.72 | 1900 | 0.6392 | |
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| 0.4691 | 2.75 | 1920 | 0.6396 | |
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| 0.2775 | 2.78 | 1940 | 0.6387 | |
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| 0.26 | 2.8 | 1960 | 0.6423 | |
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| 0.2917 | 2.83 | 1980 | 0.6432 | |
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| 0.4461 | 2.86 | 2000 | 0.6414 | |
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| 0.4149 | 2.89 | 2020 | 0.6433 | |
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| 0.2863 | 2.92 | 2040 | 0.6428 | |
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| 0.1832 | 2.95 | 2060 | 0.6424 | |
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| 0.5409 | 2.98 | 2080 | 0.6420 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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