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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 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: Llama3.1-8B-QA_CoT-MEDICAL-Instruct-r64 |
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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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# Llama3.1-8B-QA_CoT-MEDICAL-Instruct-r64 |
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This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5788 |
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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: 3.6e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 1 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:-----:|:---------------:| |
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| 0.8399 | 0.0064 | 200 | 0.9350 | |
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| 0.9024 | 0.0128 | 400 | 0.8735 | |
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| 0.7805 | 0.0192 | 600 | 0.8382 | |
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| 0.905 | 0.0256 | 800 | 0.8193 | |
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| 0.762 | 0.032 | 1000 | 0.8070 | |
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| 0.5496 | 0.0384 | 1200 | 0.7964 | |
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| 0.1024 | 0.0448 | 1400 | 0.7896 | |
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| 0.5931 | 0.0512 | 1600 | 0.7859 | |
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| 0.6966 | 0.0576 | 1800 | 0.7791 | |
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| 0.7713 | 0.064 | 2000 | 0.7753 | |
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| 0.7055 | 0.0704 | 2200 | 0.7715 | |
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| 0.9559 | 0.0768 | 2400 | 0.7681 | |
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| 0.5054 | 0.0832 | 2600 | 0.7646 | |
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| 0.7603 | 0.0896 | 2800 | 0.7622 | |
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| 0.9815 | 0.096 | 3000 | 0.7593 | |
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| 0.6948 | 0.1024 | 3200 | 0.7569 | |
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| 1.1539 | 0.1088 | 3400 | 0.7535 | |
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| 0.9092 | 0.1152 | 3600 | 0.7504 | |
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| 0.4105 | 0.1216 | 3800 | 0.7478 | |
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| 0.8514 | 0.128 | 4000 | 0.7464 | |
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| 0.6902 | 0.1344 | 4200 | 0.7431 | |
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| 0.5141 | 0.1408 | 4400 | 0.7408 | |
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| 0.5374 | 0.1472 | 4600 | 0.7393 | |
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| 0.9075 | 0.1536 | 4800 | 0.7382 | |
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| 0.4641 | 0.16 | 5000 | 0.7352 | |
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| 0.9326 | 0.1664 | 5200 | 0.7327 | |
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| 0.9203 | 0.1728 | 5400 | 0.7315 | |
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| 0.9802 | 0.1792 | 5600 | 0.7294 | |
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| 0.8411 | 0.1856 | 5800 | 0.7262 | |
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| 0.8436 | 0.192 | 6000 | 0.7234 | |
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| 0.9267 | 0.1984 | 6200 | 0.7220 | |
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| 0.8866 | 0.2048 | 6400 | 0.7203 | |
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| 0.4686 | 0.2112 | 6600 | 0.7188 | |
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| 1.1163 | 0.2176 | 6800 | 0.7171 | |
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| 0.8547 | 0.224 | 7000 | 0.7152 | |
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| 0.6537 | 0.2304 | 7200 | 0.7132 | |
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| 0.562 | 0.2368 | 7400 | 0.7117 | |
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| 0.0866 | 0.2432 | 7600 | 0.7111 | |
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| 1.259 | 0.2496 | 7800 | 0.7083 | |
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| 0.5031 | 0.256 | 8000 | 0.7078 | |
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| 0.6377 | 0.2624 | 8200 | 0.7054 | |
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| 0.664 | 0.2688 | 8400 | 0.7039 | |
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| 0.7615 | 0.2752 | 8600 | 0.7023 | |
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| 0.9197 | 0.2816 | 8800 | 0.7009 | |
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| 0.9291 | 0.288 | 9000 | 0.6991 | |
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| 0.6651 | 0.2944 | 9200 | 0.6972 | |
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| 0.9469 | 0.3008 | 9400 | 0.6965 | |
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| 0.9489 | 0.3072 | 9600 | 0.6940 | |
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| 0.4859 | 0.3136 | 9800 | 0.6931 | |
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| 0.8835 | 0.32 | 10000 | 0.6911 | |
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| 0.5059 | 0.3264 | 10200 | 0.6899 | |
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| 0.9658 | 0.3328 | 10400 | 0.6886 | |
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| 0.4364 | 0.3392 | 10600 | 0.6866 | |
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| 0.8759 | 0.3456 | 10800 | 0.6856 | |
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| 0.6947 | 0.352 | 11000 | 0.6834 | |
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| 0.9081 | 0.3584 | 11200 | 0.6818 | |
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| 0.6082 | 0.3648 | 11400 | 0.6805 | |
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| 0.7717 | 0.3712 | 11600 | 0.6789 | |
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| 0.8324 | 0.3776 | 11800 | 0.6777 | |
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| 0.6286 | 0.384 | 12000 | 0.6762 | |
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| 0.8363 | 0.3904 | 12200 | 0.6741 | |
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| 0.5502 | 0.3968 | 12400 | 0.6734 | |
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| 0.4254 | 0.4032 | 12600 | 0.6720 | |
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| 0.8676 | 0.4096 | 12800 | 0.6708 | |
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| 0.8529 | 0.416 | 13000 | 0.6693 | |
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| 0.8278 | 0.4224 | 13200 | 0.6679 | |
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| 0.561 | 0.4288 | 13400 | 0.6665 | |
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| 0.8632 | 0.4352 | 13600 | 0.6646 | |
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| 0.5054 | 0.4416 | 13800 | 0.6633 | |
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| 0.7296 | 0.448 | 14000 | 0.6614 | |
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| 0.4644 | 0.4544 | 14200 | 0.6610 | |
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| 0.851 | 0.4608 | 14400 | 0.6586 | |
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| 0.8565 | 0.4672 | 14600 | 0.6577 | |
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| 0.5205 | 0.4736 | 14800 | 0.6569 | |
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| 0.8272 | 0.48 | 15000 | 0.6550 | |
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| 0.9689 | 0.4864 | 15200 | 0.6533 | |
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| 0.7433 | 0.4928 | 15400 | 0.6525 | |
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| 0.4175 | 0.4992 | 15600 | 0.6515 | |
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| 0.8158 | 0.5056 | 15800 | 0.6500 | |
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| 0.0571 | 0.512 | 16000 | 0.6486 | |
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| 0.3766 | 0.5184 | 16200 | 0.6474 | |
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| 0.546 | 0.5248 | 16400 | 0.6457 | |
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| 0.7811 | 0.5312 | 16600 | 0.6439 | |
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| 0.6592 | 0.5376 | 16800 | 0.6427 | |
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| 0.7379 | 0.544 | 17000 | 0.6413 | |
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| 0.8019 | 0.5504 | 17200 | 0.6403 | |
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| 0.6117 | 0.5568 | 17400 | 0.6386 | |
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| 0.9152 | 0.5632 | 17600 | 0.6373 | |
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| 0.8364 | 0.5696 | 17800 | 0.6359 | |
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| 0.6807 | 0.576 | 18000 | 0.6346 | |
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| 0.3434 | 0.5824 | 18200 | 0.6337 | |
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| 0.6248 | 0.5888 | 18400 | 0.6331 | |
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| 0.4177 | 0.5952 | 18600 | 0.6320 | |
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| 0.9378 | 0.6016 | 18800 | 0.6305 | |
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| 0.5745 | 0.608 | 19000 | 0.6288 | |
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| 0.5786 | 0.6144 | 19200 | 0.6268 | |
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| 0.6503 | 0.6208 | 19400 | 0.6260 | |
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| 0.3875 | 0.6272 | 19600 | 0.6250 | |
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| 0.3826 | 0.6336 | 19800 | 0.6244 | |
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| 0.5301 | 0.64 | 20000 | 0.6231 | |
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| 0.5212 | 0.6464 | 20200 | 0.6228 | |
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| 0.2531 | 0.6528 | 20400 | 0.6210 | |
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| 0.514 | 0.6592 | 20600 | 0.6198 | |
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| 0.7908 | 0.6656 | 20800 | 0.6188 | |
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| 0.7131 | 0.672 | 21000 | 0.6174 | |
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| 0.8612 | 0.6784 | 21200 | 0.6166 | |
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| 0.5492 | 0.6848 | 21400 | 0.6153 | |
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| 0.2713 | 0.6912 | 21600 | 0.6137 | |
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| 0.3661 | 0.6976 | 21800 | 0.6122 | |
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| 0.6265 | 0.704 | 22000 | 0.6114 | |
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| 0.7062 | 0.7104 | 22200 | 0.6104 | |
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| 0.2984 | 0.7168 | 22400 | 0.6094 | |
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| 0.6959 | 0.7232 | 22600 | 0.6079 | |
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| 0.8605 | 0.7296 | 22800 | 0.6066 | |
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| 0.5706 | 0.736 | 23000 | 0.6059 | |
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| 0.5996 | 0.7424 | 23200 | 0.6044 | |
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| 0.6127 | 0.7488 | 23400 | 0.6039 | |
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| 0.3297 | 0.7552 | 23600 | 0.6034 | |
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| 0.8156 | 0.7616 | 23800 | 0.6024 | |
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| 0.5316 | 0.768 | 24000 | 0.6011 | |
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| 0.6661 | 0.7744 | 24200 | 0.6005 | |
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| 0.8805 | 0.7808 | 24400 | 0.5997 | |
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| 0.676 | 0.7872 | 24600 | 0.5981 | |
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| 0.25 | 0.7936 | 24800 | 0.5972 | |
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| 0.3684 | 0.8 | 25000 | 0.5959 | |
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| 0.5218 | 0.8064 | 25200 | 0.5951 | |
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| 0.6316 | 0.8128 | 25400 | 0.5945 | |
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| 0.2007 | 0.8192 | 25600 | 0.5936 | |
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| 0.272 | 0.8256 | 25800 | 0.5929 | |
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| 0.4253 | 0.832 | 26000 | 0.5920 | |
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| 0.4194 | 0.8384 | 26200 | 0.5908 | |
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| 0.7739 | 0.8448 | 26400 | 0.5905 | |
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| 0.4304 | 0.8512 | 26600 | 0.5895 | |
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| 0.2536 | 0.8576 | 26800 | 0.5887 | |
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| 0.794 | 0.864 | 27000 | 0.5880 | |
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| 0.3571 | 0.8704 | 27200 | 0.5872 | |
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| 0.7102 | 0.8768 | 27400 | 0.5866 | |
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| 0.3415 | 0.8832 | 27600 | 0.5858 | |
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| 0.5687 | 0.8896 | 27800 | 0.5853 | |
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| 0.6717 | 0.896 | 28000 | 0.5846 | |
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| 0.6736 | 0.9024 | 28200 | 0.5841 | |
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| 0.4328 | 0.9088 | 28400 | 0.5837 | |
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| 0.5114 | 0.9152 | 28600 | 0.5830 | |
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| 0.6912 | 0.9216 | 28800 | 0.5825 | |
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| 0.6255 | 0.928 | 29000 | 0.5820 | |
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| 0.4711 | 0.9344 | 29200 | 0.5815 | |
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| 0.6466 | 0.9408 | 29400 | 0.5810 | |
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| 0.5242 | 0.9472 | 29600 | 0.5806 | |
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| 0.5089 | 0.9536 | 29800 | 0.5802 | |
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| 0.366 | 0.96 | 30000 | 0.5798 | |
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| 0.6565 | 0.9664 | 30200 | 0.5796 | |
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| 0.7487 | 0.9728 | 30400 | 0.5794 | |
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| 0.8204 | 0.9792 | 30600 | 0.5792 | |
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| 0.8001 | 0.9856 | 30800 | 0.5790 | |
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| 0.582 | 0.992 | 31000 | 0.5789 | |
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| 0.4883 | 0.9984 | 31200 | 0.5788 | |
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### Framework versions |
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- PEFT 0.12.0 |
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- Transformers 4.47.0 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.0.0 |
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- Tokenizers 0.21.0 |
