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--- |
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library_name: peft |
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license: other |
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base_model: microsoft/Phi-3-mini-4k-instruct |
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tags: |
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- llama-factory |
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- lora |
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- generated_from_trainer |
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model-index: |
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- name: lora |
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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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# lora |
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This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on the flock_task4_tranning dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1333 |
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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: 5e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 8 |
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- total_eval_batch_size: 2 |
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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: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 1.1 | 0.0501 | 50 | 1.2970 | |
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| 1.0774 | 0.1002 | 100 | 1.2504 | |
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| 0.9563 | 0.1502 | 150 | 1.2521 | |
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| 1.0242 | 0.2003 | 200 | 1.2253 | |
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| 1.0038 | 0.2504 | 250 | 1.2113 | |
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| 0.9858 | 0.3005 | 300 | 1.1920 | |
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| 0.8694 | 0.3505 | 350 | 1.1918 | |
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| 0.9174 | 0.4006 | 400 | 1.1884 | |
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| 0.9653 | 0.4507 | 450 | 1.1870 | |
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| 0.9136 | 0.5008 | 500 | 1.1768 | |
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| 0.9014 | 0.5508 | 550 | 1.1673 | |
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| 0.9203 | 0.6009 | 600 | 1.1558 | |
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| 0.8902 | 0.6510 | 650 | 1.1679 | |
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| 0.9018 | 0.7011 | 700 | 1.1489 | |
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| 0.937 | 0.7511 | 750 | 1.1577 | |
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| 0.8984 | 0.8012 | 800 | 1.1463 | |
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| 0.8607 | 0.8513 | 850 | 1.1517 | |
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| 0.8698 | 0.9014 | 900 | 1.1436 | |
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| 0.9661 | 0.9514 | 950 | 1.1479 | |
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| 0.672 | 1.0010 | 1000 | 1.1459 | |
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| 0.8162 | 1.0511 | 1050 | 1.1374 | |
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| 0.8477 | 1.1012 | 1100 | 1.1434 | |
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| 0.9039 | 1.1512 | 1150 | 1.1394 | |
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| 0.8361 | 1.2013 | 1200 | 1.1434 | |
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| 0.8091 | 1.2514 | 1250 | 1.1391 | |
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| 0.7854 | 1.3015 | 1300 | 1.1392 | |
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| 0.7716 | 1.3515 | 1350 | 1.1403 | |
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| 0.8637 | 1.4016 | 1400 | 1.1337 | |
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| 0.8491 | 1.4517 | 1450 | 1.1392 | |
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| 0.9037 | 1.5018 | 1500 | 1.1325 | |
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| 0.8698 | 1.5518 | 1550 | 1.1371 | |
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| 0.7614 | 1.6019 | 1600 | 1.1332 | |
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| 0.7492 | 1.6520 | 1650 | 1.1350 | |
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| 0.8217 | 1.7021 | 1700 | 1.1321 | |
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| 0.8261 | 1.7521 | 1750 | 1.1323 | |
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| 0.8286 | 1.8022 | 1800 | 1.1325 | |
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| 0.8208 | 1.8523 | 1850 | 1.1335 | |
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| 0.7937 | 1.9024 | 1900 | 1.1338 | |
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| 0.837 | 1.9524 | 1950 | 1.1342 | |
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### Framework versions |
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- PEFT 0.12.0 |
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- Transformers 4.48.3 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
