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Llama-3.1-8B-Instruct-PsyCourse-fold1

PEFT
Safetensors
llama-factory
lora
Generated from Trainer
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README.md ADDED
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+ ---
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+ library_name: peft
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+ license: llama3.1
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+ base_model: meta-llama/Llama-3.1-8B-Instruct
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+ tags:
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+ - llama-factory
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+ - generated_from_trainer
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+ model-index:
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+ - name: Llama-3.1-8B-Instruct-PsyCourse-fold1
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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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+ # Llama-3.1-8B-Instruct-PsyCourse-fold1
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+
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+ This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0859
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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: 0.0001
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+ - train_batch_size: 1
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+ - eval_batch_size: 1
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+ - seed: 42
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+ - gradient_accumulation_steps: 16
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+ - total_train_batch_size: 16
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+ - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: cosine
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 10.0
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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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+ | 0.8899 | 0.0770 | 50 | 0.7211 |
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+ | 0.2218 | 0.1539 | 100 | 0.1442 |
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+ | 0.0967 | 0.2309 | 150 | 0.0795 |
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+ | 0.0791 | 0.3078 | 200 | 0.0597 |
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+ | 0.0625 | 0.3848 | 250 | 0.0552 |
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+ | 0.0557 | 0.4617 | 300 | 0.0504 |
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+ | 0.0518 | 0.5387 | 350 | 0.0469 |
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+ | 0.0693 | 0.6156 | 400 | 0.0492 |
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+ | 0.0451 | 0.6926 | 450 | 0.0476 |
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+ | 0.0404 | 0.7695 | 500 | 0.0453 |
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+ | 0.053 | 0.8465 | 550 | 0.0436 |
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+ | 0.0404 | 0.9234 | 600 | 0.0420 |
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+ | 0.0397 | 1.0004 | 650 | 0.0416 |
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+ | 0.0468 | 1.0773 | 700 | 0.0426 |
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+ | 0.0375 | 1.1543 | 750 | 0.0389 |
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+ | 0.0352 | 1.2312 | 800 | 0.0376 |
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+ | 0.0397 | 1.3082 | 850 | 0.0402 |
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+ | 0.0262 | 1.3851 | 900 | 0.0378 |
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+ | 0.0496 | 1.4621 | 950 | 0.0389 |
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+ | 0.0395 | 1.5391 | 1000 | 0.0366 |
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+ | 0.0382 | 1.6160 | 1050 | 0.0368 |
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+ | 0.0403 | 1.6930 | 1100 | 0.0389 |
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+ | 0.0251 | 1.7699 | 1150 | 0.0373 |
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+ | 0.0259 | 1.8469 | 1200 | 0.0392 |
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+ | 0.0354 | 1.9238 | 1250 | 0.0383 |
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+ | 0.0289 | 2.0008 | 1300 | 0.0360 |
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+ | 0.0205 | 2.0777 | 1350 | 0.0348 |
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+ | 0.0356 | 2.1547 | 1400 | 0.0374 |
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+ | 0.0136 | 2.2316 | 1450 | 0.0403 |
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+ | 0.0276 | 2.3086 | 1500 | 0.0372 |
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+ | 0.0198 | 2.3855 | 1550 | 0.0404 |
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+ | 0.0211 | 2.4625 | 1600 | 0.0372 |
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+ | 0.0293 | 2.5394 | 1650 | 0.0363 |
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+ | 0.0331 | 2.6164 | 1700 | 0.0349 |
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+ | 0.0339 | 2.6933 | 1750 | 0.0367 |
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+ | 0.0314 | 2.7703 | 1800 | 0.0353 |
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+ | 0.0375 | 2.8472 | 1850 | 0.0351 |
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+ | 0.0265 | 2.9242 | 1900 | 0.0376 |
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+ | 0.0408 | 3.0012 | 1950 | 0.0380 |
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+ | 0.0133 | 3.0781 | 2000 | 0.0460 |
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+ | 0.0213 | 3.1551 | 2050 | 0.0390 |
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+ | 0.0197 | 3.2320 | 2100 | 0.0450 |
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+ | 0.0205 | 3.3090 | 2150 | 0.0417 |
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+ | 0.0236 | 3.3859 | 2200 | 0.0411 |
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+ | 0.0168 | 3.4629 | 2250 | 0.0420 |
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+ | 0.0185 | 3.5398 | 2300 | 0.0419 |
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+ | 0.0116 | 3.6168 | 2350 | 0.0409 |
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+ | 0.019 | 3.6937 | 2400 | 0.0425 |
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+ | 0.0183 | 3.7707 | 2450 | 0.0406 |
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+ | 0.0137 | 3.8476 | 2500 | 0.0455 |
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+ | 0.0303 | 3.9246 | 2550 | 0.0398 |
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+ | 0.0186 | 4.0015 | 2600 | 0.0417 |
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+ | 0.0061 | 4.0785 | 2650 | 0.0473 |
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+ | 0.0134 | 4.1554 | 2700 | 0.0502 |
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+ | 0.0108 | 4.2324 | 2750 | 0.0512 |
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+ | 0.0097 | 4.3093 | 2800 | 0.0657 |
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+ | 0.0075 | 4.3863 | 2850 | 0.0519 |
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+ | 0.0111 | 4.4633 | 2900 | 0.0445 |
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+ | 0.0063 | 4.5402 | 2950 | 0.0479 |
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+ | 0.0089 | 4.6172 | 3000 | 0.0525 |
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+ | 0.0163 | 4.6941 | 3050 | 0.0442 |
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+ | 0.0121 | 4.7711 | 3100 | 0.0471 |
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+ | 0.0156 | 4.8480 | 3150 | 0.0475 |
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+ | 0.0066 | 4.9250 | 3200 | 0.0483 |
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+ | 0.0055 | 5.0019 | 3250 | 0.0464 |
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+ | 0.0056 | 5.0789 | 3300 | 0.0561 |
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+ | 0.0033 | 5.1558 | 3350 | 0.0575 |
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+ | 0.0035 | 5.2328 | 3400 | 0.0597 |
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+ | 0.004 | 5.3097 | 3450 | 0.0622 |
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+ | 0.0052 | 5.3867 | 3500 | 0.0530 |
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+ | 0.0025 | 5.4636 | 3550 | 0.0536 |
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+ | 0.006 | 5.5406 | 3600 | 0.0567 |
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+ | 0.0103 | 5.6175 | 3650 | 0.0531 |
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+ | 0.0046 | 5.6945 | 3700 | 0.0585 |
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+ | 0.0077 | 5.7715 | 3750 | 0.0547 |
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+ | 0.0058 | 5.8484 | 3800 | 0.0545 |
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+ | 0.0033 | 5.9254 | 3850 | 0.0545 |
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+ | 0.0041 | 6.0023 | 3900 | 0.0512 |
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+ | 0.0024 | 6.0793 | 3950 | 0.0595 |
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+ | 0.0018 | 6.1562 | 4000 | 0.0629 |
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+ | 0.0046 | 6.2332 | 4050 | 0.0674 |
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+ | 0.0003 | 6.3101 | 4100 | 0.0636 |
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+ | 0.001 | 6.3871 | 4150 | 0.0662 |
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+ | 0.0004 | 6.4640 | 4200 | 0.0726 |
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+ | 0.0027 | 6.5410 | 4250 | 0.0641 |
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+ | 0.0033 | 6.6179 | 4300 | 0.0715 |
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+ | 0.004 | 6.6949 | 4350 | 0.0666 |
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+ | 0.0028 | 6.7718 | 4400 | 0.0737 |
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+ | 0.0015 | 6.8488 | 4450 | 0.0720 |
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+ | 0.0017 | 6.9257 | 4500 | 0.0645 |
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+ | 0.0025 | 7.0027 | 4550 | 0.0586 |
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+ | 0.0003 | 7.0796 | 4600 | 0.0643 |
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+ | 0.0009 | 7.1566 | 4650 | 0.0701 |
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+ | 0.0005 | 7.2336 | 4700 | 0.0749 |
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+ | 0.0003 | 7.3105 | 4750 | 0.0769 |
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+ | 0.0002 | 7.3875 | 4800 | 0.0769 |
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+ | 0.0019 | 7.4644 | 4850 | 0.0791 |
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+ | 0.0025 | 7.5414 | 4900 | 0.0791 |
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+ | 0.0023 | 7.6183 | 4950 | 0.0682 |
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+ | 0.0001 | 7.6953 | 5000 | 0.0733 |
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+ | 0.0013 | 7.7722 | 5050 | 0.0719 |
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+ | 0.0004 | 7.8492 | 5100 | 0.0716 |
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+ | 0.0008 | 7.9261 | 5150 | 0.0736 |
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+ | 0.0017 | 8.0031 | 5200 | 0.0732 |
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+ | 0.0007 | 8.0800 | 5250 | 0.0744 |
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+ | 0.0006 | 8.1570 | 5300 | 0.0763 |
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+ | 0.0021 | 8.2339 | 5350 | 0.0778 |
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+ | 0.0013 | 8.3109 | 5400 | 0.0789 |
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+ | 0.0008 | 8.3878 | 5450 | 0.0801 |
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+ | 0.0013 | 8.4648 | 5500 | 0.0794 |
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+ | 0.0002 | 8.5417 | 5550 | 0.0810 |
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+ | 0.0005 | 8.6187 | 5600 | 0.0816 |
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+ | 0.0 | 8.6957 | 5650 | 0.0819 |
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+ | 0.0002 | 8.7726 | 5700 | 0.0827 |
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+ | 0.0002 | 8.8496 | 5750 | 0.0826 |
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+ | 0.0001 | 8.9265 | 5800 | 0.0829 |
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+ | 0.0001 | 9.0035 | 5850 | 0.0829 |
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+ | 0.0001 | 9.0804 | 5900 | 0.0833 |
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+ | 0.0001 | 9.1574 | 5950 | 0.0842 |
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+ | 0.0004 | 9.2343 | 6000 | 0.0847 |
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+ | 0.0007 | 9.3113 | 6050 | 0.0851 |
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+ | 0.0002 | 9.3882 | 6100 | 0.0856 |
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+ | 0.0001 | 9.4652 | 6150 | 0.0855 |
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+ | 0.0001 | 9.5421 | 6200 | 0.0859 |
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+ | 0.0003 | 9.6191 | 6250 | 0.0859 |
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+ | 0.0001 | 9.6960 | 6300 | 0.0859 |
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+ | 0.0003 | 9.7730 | 6350 | 0.0861 |
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+ | 0.0008 | 9.8499 | 6400 | 0.0859 |
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+ | 0.0006 | 9.9269 | 6450 | 0.0859 |
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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 4.46.1
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+ - Pytorch 2.5.1+cu124
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+ - Datasets 3.1.0
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+ - Tokenizers 0.20.3
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