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Phi-Medical-QA-LoRA
aryaadhi/Phi-Medical-QA-LoRA
Phi-Medical-QA-LoRA is a machine learning model from aryaadhi. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- peft
- License
- mit
- Access
- Public
- Created
- Jul 12, 2024
- Updated
- Jul 22, 2024
- SHA
- 715f9263
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
Phi-Medical-QA-LoRA
This model is a fine-tuned version of microsoft/phi-1_5 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.7010
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 7
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.1348 | 0.1684 | 100 | 2.7621 |
| 2.6302 | 0.3367 | 200 | 2.5110 |
| 2.4441 | 0.5051 | 300 | 2.3619 |
| 2.3325 | 0.6734 | 400 | 2.2485 |
| 2.2262 | 0.8418 | 500 | 2.1819 |
| 2.1809 | 1.0101 | 600 | 2.1318 |
| 2.0646 | 1.1785 | 700 | 2.0802 |
| 2.0665 | 1.3468 | 800 | 2.0541 |
| 2.0072 | 1.5152 | 900 | 2.0057 |
| 1.9954 | 1.6835 | 1000 | 1.9762 |
| 1.9577 | 1.8519 | 1100 | 1.9554 |
| 1.9263 | 2.0202 | 1200 | 1.9256 |
| 1.8635 | 2.1886 | 1300 | 1.9027 |
| 1.855 | 2.3569 | 1400 | 1.8871 |
| 1.8258 | 2.5253 | 1500 | 1.8750 |
| 1.8269 | 2.6936 | 1600 | 1.8555 |
| 1.8194 | 2.8620 | 1700 | 1.8415 |
| 1.775 | 3.0303 | 1800 | 1.8257 |
| 1.7379 | 3.1987 | 1900 | 1.8175 |
| 1.7384 | 3.3670 | 2000 | 1.8052 |
| 1.74 | 3.5354 | 2100 | 1.7943 |
| 1.7275 | 3.7037 | 2200 | 1.7778 |
| 1.6903 | 3.8721 | 2300 | 1.7680 |
| 1.6908 | 4.0404 | 2400 | 1.7594 |
| 1.6663 | 4.2088 | 2500 | 1.7559 |
| 1.6312 | 4.3771 | 2600 | 1.7457 |
| 1.6412 | 4.5455 | 2700 | 1.7395 |
| 1.6392 | 4.7138 | 2800 | 1.7327 |
| 1.6237 | 4.8822 | 2900 | 1.7260 |
| 1.6138 | 5.0505 | 3000 | 1.7244 |
| 1.5858 | 5.2189 | 3100 | 1.7205 |
| 1.6005 | 5.3872 | 3200 | 1.7163 |
| 1.5662 | 5.5556 | 3300 | 1.7120 |
| 1.5888 | 5.7239 | 3400 | 1.7075 |
| 1.5802 | 5.8923 | 3500 | 1.7068 |
| 1.5659 | 6.0606 | 3600 | 1.7038 |
| 1.5526 | 6.2290 | 3700 | 1.7039 |
| 1.54 | 6.3973 | 3800 | 1.7024 |
| 1.5653 | 6.5657 | 3900 | 1.7018 |
| 1.545 | 6.7340 | 4000 | 1.7012 |
| 1.5455 | 6.9024 | 4100 | 1.7010 |
Framework versions
- PEFT 0.11.1
- Transformers 4.42.4
- Pytorch 1.13.1+cu117
- Datasets 2.19.2
- Tokenizers 0.19.1
- Library
- peft
- License
- mit
- Created
- Jul 12, 2024
- Updated
- Jul 22, 2024