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SangMoone/beomi-ko-gemma_train_test_33000

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  2. README.md +189 -0
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.gitattributes CHANGED
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README.md ADDED
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+ ---
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+ base_model: beomi/gemma-ko-2b
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+ library_name: peft
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+ license: other
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+ tags:
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+ - trl
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+ - sft
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+ - generated_from_trainer
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+ model-index:
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+ - name: results_1011
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+ results: []
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+ ---
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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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+
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+ # results_1011
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+
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+ This model is a fine-tuned version of [beomi/gemma-ko-2b](https://huggingface.co/beomi/gemma-ko-2b) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.5227
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 3
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+ - eval_batch_size: 2
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+ - seed: 42
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+ - gradient_accumulation_steps: 8
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+ - total_train_batch_size: 24
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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_ratio: 0.03
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+ - num_epochs: 10
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:------:|:-----:|:---------------:|
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+ | 2.0356 | 0.08 | 100 | 1.8434 |
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+ | 1.7068 | 0.16 | 200 | 1.6624 |
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+ | 1.6553 | 0.24 | 300 | 1.6477 |
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+ | 1.6456 | 0.32 | 400 | 1.6441 |
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+ | 1.6403 | 0.4 | 500 | 1.6378 |
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+ | 1.6347 | 0.48 | 600 | 1.6315 |
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+ | 1.6218 | 0.56 | 700 | 1.6256 |
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+ | 1.6259 | 0.64 | 800 | 1.6192 |
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+ | 1.6156 | 0.72 | 900 | 1.6116 |
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+ | 1.6202 | 0.8 | 1000 | 1.6075 |
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+ | 1.6031 | 0.88 | 1100 | 1.6058 |
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+ | 1.6018 | 0.96 | 1200 | 1.6031 |
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+ | 1.5965 | 1.04 | 1300 | 1.6022 |
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+ | 1.5988 | 1.12 | 1400 | 1.6002 |
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+ | 1.6043 | 1.2 | 1500 | 1.5978 |
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+ | 1.5933 | 1.28 | 1600 | 1.5962 |
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+ | 1.5909 | 1.3600 | 1700 | 1.5953 |
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+ | 1.6014 | 1.44 | 1800 | 1.5932 |
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+ | 1.584 | 1.52 | 1900 | 1.5912 |
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+ | 1.5865 | 1.6 | 2000 | 1.5897 |
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+ | 1.5871 | 1.6800 | 2100 | 1.5880 |
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+ | 1.5838 | 1.76 | 2200 | 1.5865 |
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+ | 1.5878 | 1.8400 | 2300 | 1.5850 |
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+ | 1.58 | 1.92 | 2400 | 1.5835 |
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+ | 1.5819 | 2.0 | 2500 | 1.5812 |
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+ | 1.5652 | 2.08 | 2600 | 1.5806 |
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+ | 1.573 | 2.16 | 2700 | 1.5796 |
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+ | 1.5677 | 2.24 | 2800 | 1.5779 |
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+ | 1.572 | 2.32 | 2900 | 1.5764 |
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+ | 1.5688 | 2.4 | 3000 | 1.5748 |
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+ | 1.5663 | 2.48 | 3100 | 1.5730 |
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+ | 1.5669 | 2.56 | 3200 | 1.5719 |
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+ | 1.5613 | 2.64 | 3300 | 1.5704 |
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+ | 1.564 | 2.7200 | 3400 | 1.5690 |
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+ | 1.5619 | 2.8 | 3500 | 1.5681 |
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+ | 1.5622 | 2.88 | 3600 | 1.5667 |
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+ | 1.5628 | 2.96 | 3700 | 1.5651 |
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+ | 1.5514 | 3.04 | 3800 | 1.5645 |
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+ | 1.5597 | 3.12 | 3900 | 1.5628 |
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+ | 1.5499 | 3.2 | 4000 | 1.5622 |
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+ | 1.5436 | 3.2800 | 4100 | 1.5610 |
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+ | 1.5521 | 3.36 | 4200 | 1.5598 |
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+ | 1.5389 | 3.44 | 4300 | 1.5585 |
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+ | 1.5518 | 3.52 | 4400 | 1.5577 |
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+ | 1.545 | 3.6 | 4500 | 1.5559 |
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+ | 1.5383 | 3.68 | 4600 | 1.5552 |
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+ | 1.5338 | 3.76 | 4700 | 1.5538 |
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+ | 1.5452 | 3.84 | 4800 | 1.5522 |
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+ | 1.5269 | 3.92 | 4900 | 1.5516 |
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+ | 1.5342 | 4.0 | 5000 | 1.5507 |
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+ | 1.5243 | 4.08 | 5100 | 1.5503 |
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+ | 1.5209 | 4.16 | 5200 | 1.5498 |
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+ | 1.5337 | 4.24 | 5300 | 1.5487 |
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+ | 1.5261 | 4.32 | 5400 | 1.5477 |
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+ | 1.5255 | 4.4 | 5500 | 1.5463 |
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+ | 1.5342 | 4.48 | 5600 | 1.5459 |
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+ | 1.5211 | 4.5600 | 5700 | 1.5447 |
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+ | 1.5293 | 4.64 | 5800 | 1.5441 |
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+ | 1.5203 | 4.72 | 5900 | 1.5425 |
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+ | 1.5171 | 4.8 | 6000 | 1.5421 |
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+ | 1.5239 | 4.88 | 6100 | 1.5412 |
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+ | 1.5184 | 4.96 | 6200 | 1.5404 |
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+ | 1.508 | 5.04 | 6300 | 1.5405 |
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+ | 1.5113 | 5.12 | 6400 | 1.5396 |
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+ | 1.5035 | 5.2 | 6500 | 1.5385 |
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+ | 1.5082 | 5.28 | 6600 | 1.5380 |
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+ | 1.5144 | 5.36 | 6700 | 1.5376 |
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+ | 1.5052 | 5.44 | 6800 | 1.5367 |
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+ | 1.5096 | 5.52 | 6900 | 1.5358 |
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+ | 1.5139 | 5.6 | 7000 | 1.5348 |
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+ | 1.5026 | 5.68 | 7100 | 1.5344 |
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+ | 1.5061 | 5.76 | 7200 | 1.5339 |
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+ | 1.5073 | 5.84 | 7300 | 1.5332 |
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+ | 1.5082 | 5.92 | 7400 | 1.5323 |
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+ | 1.5118 | 6.0 | 7500 | 1.5320 |
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+ | 1.4939 | 6.08 | 7600 | 1.5323 |
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+ | 1.4986 | 6.16 | 7700 | 1.5322 |
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+ | 1.492 | 6.24 | 7800 | 1.5324 |
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+ | 1.4889 | 6.32 | 7900 | 1.5309 |
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+ | 1.4986 | 6.4 | 8000 | 1.5301 |
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+ | 1.5003 | 6.48 | 8100 | 1.5297 |
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+ | 1.5059 | 6.5600 | 8200 | 1.5295 |
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+ | 1.4961 | 6.64 | 8300 | 1.5291 |
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+ | 1.4938 | 6.72 | 8400 | 1.5279 |
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+ | 1.5039 | 6.8 | 8500 | 1.5276 |
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+ | 1.4892 | 6.88 | 8600 | 1.5272 |
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+ | 1.5 | 6.96 | 8700 | 1.5268 |
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+ | 1.4944 | 7.04 | 8800 | 1.5270 |
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+ | 1.4941 | 7.12 | 8900 | 1.5265 |
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+ | 1.4849 | 7.2 | 9000 | 1.5270 |
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+ | 1.4924 | 7.28 | 9100 | 1.5261 |
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+ | 1.4903 | 7.36 | 9200 | 1.5256 |
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+ | 1.4909 | 7.44 | 9300 | 1.5254 |
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+ | 1.4884 | 7.52 | 9400 | 1.5253 |
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+ | 1.4874 | 7.6 | 9500 | 1.5253 |
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+ | 1.4973 | 7.68 | 9600 | 1.5251 |
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+ | 1.4835 | 7.76 | 9700 | 1.5247 |
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+ | 1.4844 | 7.84 | 9800 | 1.5245 |
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+ | 1.4845 | 7.92 | 9900 | 1.5242 |
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+ | 1.4857 | 8.0 | 10000 | 1.5239 |
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+ | 1.483 | 8.08 | 10100 | 1.5241 |
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+ | 1.4875 | 8.16 | 10200 | 1.5238 |
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+ | 1.488 | 8.24 | 10300 | 1.5238 |
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+ | 1.4816 | 8.32 | 10400 | 1.5236 |
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+ | 1.4887 | 8.4 | 10500 | 1.5233 |
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+ | 1.4785 | 8.48 | 10600 | 1.5236 |
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+ | 1.4802 | 8.56 | 10700 | 1.5232 |
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+ | 1.4846 | 8.64 | 10800 | 1.5231 |
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+ | 1.4832 | 8.72 | 10900 | 1.5231 |
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+ | 1.4821 | 8.8 | 11000 | 1.5229 |
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+ | 1.4837 | 8.88 | 11100 | 1.5230 |
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+ | 1.4865 | 8.96 | 11200 | 1.5229 |
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+ | 1.4855 | 9.04 | 11300 | 1.5228 |
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+ | 1.4841 | 9.12 | 11400 | 1.5229 |
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+ | 1.4765 | 9.2 | 11500 | 1.5230 |
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+ | 1.4795 | 9.28 | 11600 | 1.5228 |
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+ | 1.4848 | 9.36 | 11700 | 1.5228 |
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+ | 1.4827 | 9.44 | 11800 | 1.5229 |
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+ | 1.4883 | 9.52 | 11900 | 1.5228 |
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+ | 1.4796 | 9.6 | 12000 | 1.5228 |
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+ | 1.4899 | 9.68 | 12100 | 1.5228 |
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+ | 1.4852 | 9.76 | 12200 | 1.5227 |
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+ | 1.48 | 9.84 | 12300 | 1.5227 |
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+ | 1.4823 | 9.92 | 12400 | 1.5227 |
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+ | 1.4796 | 10.0 | 12500 | 1.5227 |
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+
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+
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+ ### Framework versions
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+
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+ - PEFT 0.12.0
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+ - Transformers 4.45.0
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+ - Pytorch 2.4.0+cu121
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+ - Datasets 2.21.0
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+ - Tokenizers 0.20.1
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