neurips-2023-llm-efficiency
Collection
Fine-tune models, datasets and artifacts used for llm efficiency competition.
https://llm-efficiency-challenge.github.io/challenge
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15 items
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Updated
This model is a fine-tuned version of meta-llama/Llama-2-13b-hf on the None dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
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 |
Base model
meta-llama/Llama-2-13b-hf