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paligemma_vqav2

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Magneto/Paligemma_Image_Caption_Finetune

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  1. README.md +234 -0
  2. adapter_config.json +34 -0
  3. adapter_model.safetensors +3 -0
  4. runs/Jun03_10-57-44_6cb4cba150ae/events.out.tfevents.1717412265.6cb4cba150ae.396.0 +3 -0
  5. runs/Jun03_11-01-08_6cb4cba150ae/events.out.tfevents.1717412469.6cb4cba150ae.5027.0 +3 -0
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  19. runs/Jun03_13-01-15_e5c1ea114b39/events.out.tfevents.1717419676.e5c1ea114b39.27388.0 +3 -0
  20. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ license: gemma
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+ library_name: peft
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+ tags:
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+ - generated_from_trainer
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+ base_model: google/paligemma-3b-pt-224
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+ model-index:
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+ - name: paligemma_vqav2
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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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+ # paligemma_vqav2
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+
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+ This model is a fine-tuned version of [google/paligemma-3b-pt-224](https://huggingface.co/google/paligemma-3b-pt-224) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.0115
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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: 2e-05
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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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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 2
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+ - num_epochs: 2
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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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+ | 2.6221 | 0.0114 | 50 | 2.4036 |
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+ | 2.244 | 0.0228 | 100 | 2.0980 |
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+ | 2.0078 | 0.0343 | 150 | 1.8881 |
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+ | 1.8561 | 0.0457 | 200 | 1.7707 |
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+ | 1.6108 | 0.0571 | 250 | 1.6833 |
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+ | 1.5712 | 0.0685 | 300 | 1.6297 |
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+ | 1.6298 | 0.0800 | 350 | 1.5834 |
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+ | 1.469 | 0.0914 | 400 | 1.5454 |
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+ | 1.4758 | 0.1028 | 450 | 1.5210 |
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+ | 1.5303 | 0.1142 | 500 | 1.4936 |
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+ | 1.3559 | 0.1257 | 550 | 1.4793 |
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+ | 1.4407 | 0.1371 | 600 | 1.4596 |
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+ | 1.4655 | 0.1485 | 650 | 1.4360 |
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+ | 1.4213 | 0.1599 | 700 | 1.4223 |
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+ | 1.3744 | 0.1714 | 750 | 1.4022 |
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+ | 1.4285 | 0.1828 | 800 | 1.3906 |
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+ | 1.2105 | 0.1942 | 850 | 1.3790 |
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+ | 1.3653 | 0.2056 | 900 | 1.3687 |
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+ | 1.337 | 0.2170 | 950 | 1.3602 |
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+ | 1.1845 | 0.2285 | 1000 | 1.3509 |
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+ | 1.3404 | 0.2399 | 1050 | 1.3384 |
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+ | 1.2957 | 0.2513 | 1100 | 1.3278 |
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+ | 1.2107 | 0.2627 | 1150 | 1.3176 |
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+ | 1.4208 | 0.2742 | 1200 | 1.3132 |
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+ | 1.2522 | 0.2856 | 1250 | 1.3032 |
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+ | 1.2735 | 0.2970 | 1300 | 1.2992 |
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+ | 1.3567 | 0.3084 | 1350 | 1.2854 |
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+ | 1.0994 | 0.3199 | 1400 | 1.2805 |
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+ | 1.2496 | 0.3313 | 1450 | 1.2710 |
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+ | 1.1944 | 0.3427 | 1500 | 1.2660 |
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+ | 1.3303 | 0.3541 | 1550 | 1.2610 |
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+ | 1.2942 | 0.3655 | 1600 | 1.2524 |
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+ | 1.2187 | 0.3770 | 1650 | 1.2458 |
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+ | 1.2071 | 0.3884 | 1700 | 1.2395 |
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+ | 1.1734 | 0.3998 | 1750 | 1.2356 |
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+ | 1.182 | 0.4112 | 1800 | 1.2301 |
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+ | 1.2104 | 0.4227 | 1850 | 1.2302 |
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+ | 1.1961 | 0.4341 | 1900 | 1.2258 |
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+ | 1.1749 | 0.4455 | 1950 | 1.2244 |
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+ | 1.1283 | 0.4569 | 2000 | 1.2189 |
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+ | 1.095 | 0.4684 | 2050 | 1.2174 |
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+ | 1.1376 | 0.4798 | 2100 | 1.2172 |
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+ | 1.0772 | 0.4912 | 2150 | 1.2137 |
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+ | 1.255 | 0.5026 | 2200 | 1.2111 |
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+ | 1.1682 | 0.5141 | 2250 | 1.2076 |
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+ | 1.1455 | 0.5255 | 2300 | 1.2052 |
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+ | 1.151 | 0.5369 | 2350 | 1.2034 |
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+ | 0.9805 | 0.5483 | 2400 | 1.2007 |
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+ | 1.1706 | 0.5597 | 2450 | 1.1985 |
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+ | 1.1961 | 0.5712 | 2500 | 1.1960 |
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+ | 1.0449 | 0.5826 | 2550 | 1.1937 |
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+ | 1.1375 | 0.5940 | 2600 | 1.1908 |
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+ | 1.1205 | 0.6054 | 2650 | 1.1896 |
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+ | 1.2097 | 0.6169 | 2700 | 1.1908 |
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+ | 1.1976 | 0.6283 | 2750 | 1.1856 |
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+ | 1.1327 | 0.6397 | 2800 | 1.0918 |
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+ | 1.0446 | 0.6511 | 2850 | 1.0929 |
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+ | 1.0804 | 0.6626 | 2900 | 1.0878 |
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+ | 0.9446 | 0.6740 | 2950 | 1.0871 |
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+ | 1.0722 | 0.6854 | 3000 | 1.0851 |
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+ | 1.1224 | 0.6968 | 3050 | 1.0865 |
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+ | 1.2711 | 0.7082 | 3100 | 1.0826 |
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+ | 1.0378 | 0.7197 | 3150 | 1.0835 |
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+ | 1.0873 | 0.7311 | 3200 | 1.0823 |
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+ | 1.1336 | 0.7425 | 3250 | 1.0815 |
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+ | 1.1407 | 0.7539 | 3300 | 1.0782 |
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+ | 1.0805 | 0.7654 | 3350 | 1.0786 |
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+ | 1.2204 | 0.7768 | 3400 | 1.0773 |
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+ | 1.0855 | 0.7882 | 3450 | 1.1838 |
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+ | 1.1151 | 0.7996 | 3500 | 1.1843 |
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+ | 1.01 | 0.8111 | 3550 | 1.1815 |
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+ | 1.1389 | 0.8225 | 3600 | 1.1828 |
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+ | 1.0022 | 0.8568 | 3750 | 1.1764 |
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+ | 1.0751 | 0.8682 | 3800 | 1.1764 |
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+ | 0.9681 | 0.8796 | 3850 | 1.1764 |
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+ | 1.101 | 0.8910 | 3900 | 1.1740 |
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+ | 1.0931 | 0.9024 | 3950 | 1.1730 |
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+ | 1.0791 | 0.9139 | 4000 | 1.1721 |
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+ | 1.1226 | 0.9824 | 4300 | 1.1670 |
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+ | 1.1432 | 0.9938 | 4350 | 1.1641 |
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+ | 1.1632 | 1.0053 | 4400 | 1.1614 |
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+ | 0.9927 | 1.0167 | 4450 | 1.1600 |
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+ | 0.9685 | 1.0281 | 4500 | 1.1559 |
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+ | 1.1403 | 1.0395 | 4550 | 1.1563 |
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+ | 1.0136 | 1.0852 | 4750 | 1.1521 |
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+ | 1.0297 | 1.0966 | 4800 | 1.1519 |
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+ | 1.1304 | 1.1081 | 4850 | 1.1508 |
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+ | 1.2172 | 1.1195 | 4900 | 1.1517 |
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+ | 1.0156 | 1.1309 | 4950 | 1.1511 |
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+ | 1.0726 | 1.1423 | 5000 | 1.1483 |
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+ | 1.0272 | 1.1538 | 5050 | 1.0159 |
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+ | 1.1042 | 1.1652 | 5100 | 1.0153 |
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+ | 1.0118 | 1.1766 | 5150 | 1.0127 |
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+ | 1.1269 | 1.1880 | 5200 | 1.0148 |
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+ | 1.0389 | 1.1995 | 5250 | 1.0152 |
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+ | 1.1804 | 1.2109 | 5300 | 1.0154 |
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+ | 1.1138 | 1.2223 | 5350 | 1.0153 |
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+ | 1.0319 | 1.2337 | 5400 | 1.0144 |
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+ | 1.0 | 1.2451 | 5450 | 1.0153 |
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+ | 1.1573 | 1.2566 | 5500 | 1.0152 |
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+ | 1.0604 | 1.2680 | 5550 | 1.0126 |
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+ | 1.081 | 1.2794 | 5600 | 1.0118 |
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+ | 0.988 | 1.2908 | 5650 | 1.0126 |
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+ | 1.1302 | 1.3023 | 5700 | 1.0119 |
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+ | 1.0626 | 1.3137 | 5750 | 1.0129 |
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+ | 1.051 | 1.3251 | 5800 | 1.0100 |
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+ | 1.0849 | 1.3365 | 5850 | 1.0094 |
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+ | 1.0739 | 1.3480 | 5900 | 1.0090 |
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+ | 1.0457 | 1.3594 | 5950 | 1.0074 |
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+ | 1.0924 | 1.3708 | 6000 | 1.0090 |
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+ | 0.9545 | 1.3822 | 6050 | 1.0084 |
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+ | 1.0727 | 1.3936 | 6100 | 1.0076 |
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+ | 1.1274 | 1.4051 | 6150 | 1.0075 |
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+ | 1.0515 | 1.4165 | 6200 | 1.0066 |
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+ | 0.9465 | 1.4279 | 6250 | 1.0057 |
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+ | 1.029 | 1.4393 | 6300 | 1.0062 |
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+ | 1.0454 | 1.4508 | 6350 | 1.0058 |
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+ | 0.9563 | 1.4622 | 6400 | 1.0053 |
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+ | 1.1052 | 1.4736 | 6450 | 1.0049 |
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+ | 0.9351 | 1.4850 | 6500 | 1.0059 |
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+ | 1.0206 | 1.5079 | 6600 | 1.0039 |
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+ | 0.983 | 1.8734 | 8200 | 1.0119 |
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+ | 1.1011 | 1.8963 | 8300 | 1.0123 |
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+ | 1.0133 | 1.9305 | 8450 | 1.0117 |
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+ | 1.0856 | 1.9420 | 8500 | 1.0118 |
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+ | 0.9716 | 1.9762 | 8650 | 1.0121 |
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+ | 1.0925 | 1.9877 | 8700 | 1.0117 |
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+ | 1.0235 | 1.9991 | 8750 | 1.0115 |
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+
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+
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+ ### Framework versions
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+
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+ - PEFT 0.11.1
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+ - Transformers 4.42.0.dev0
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+ - Pytorch 2.3.0+cu121
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+ - Datasets 2.19.2
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+ - Tokenizers 0.19.1
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