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phi-3-mini-LoRA-MEDQA-Extended-V3
KrithikV/phi-3-mini-LoRA-MEDQA-Extended-V3
phi-3-mini-LoRA-MEDQA-Extended-V3 is a machine learning model from KrithikV. 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
- Aug 3, 2024
- Updated
- Aug 3, 2024
- SHA
- 1b73d551
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-3-mini-LoRA-MEDQA-Extended-V3
This model is a fine-tuned version of microsoft/Phi-3-mini-4k-instruct on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6233
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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.7825 | 0.0882 | 200 | 0.6760 |
| 0.6593 | 0.1764 | 400 | 0.6488 |
| 0.6454 | 0.2646 | 600 | 0.6424 |
| 0.6424 | 0.3528 | 800 | 0.6382 |
| 0.6382 | 0.4410 | 1000 | 0.6358 |
| 0.6342 | 0.5292 | 1200 | 0.6340 |
| 0.6355 | 0.6174 | 1400 | 0.6327 |
| 0.6355 | 0.7055 | 1600 | 0.6315 |
| 0.6336 | 0.7937 | 1800 | 0.6307 |
| 0.6321 | 0.8819 | 2000 | 0.6298 |
| 0.6321 | 0.9701 | 2200 | 0.6291 |
| 0.6298 | 1.0583 | 2400 | 0.6286 |
| 0.6285 | 1.1465 | 2600 | 0.6280 |
| 0.628 | 1.2347 | 2800 | 0.6275 |
| 0.6282 | 1.3229 | 3000 | 0.6271 |
| 0.6278 | 1.4111 | 3200 | 0.6267 |
| 0.6257 | 1.4993 | 3400 | 0.6264 |
| 0.6276 | 1.5875 | 3600 | 0.6260 |
| 0.6253 | 1.6757 | 3800 | 0.6256 |
| 0.6253 | 1.7639 | 4000 | 0.6253 |
| 0.6242 | 1.8521 | 4200 | 0.6250 |
| 0.6252 | 1.9402 | 4400 | 0.6247 |
| 0.6239 | 2.0284 | 4600 | 0.6246 |
| 0.6222 | 2.1166 | 4800 | 0.6244 |
| 0.6226 | 2.2048 | 5000 | 0.6242 |
| 0.6219 | 2.2930 | 5200 | 0.6241 |
| 0.6227 | 2.3812 | 5400 | 0.6240 |
| 0.6195 | 2.4694 | 5600 | 0.6239 |
| 0.6219 | 2.5576 | 5800 | 0.6237 |
| 0.6221 | 2.6458 | 6000 | 0.6236 |
| 0.6238 | 2.7340 | 6200 | 0.6235 |
| 0.621 | 2.8222 | 6400 | 0.6234 |
| 0.621 | 2.9104 | 6600 | 0.6234 |
| 0.6222 | 2.9986 | 6800 | 0.6233 |
Framework versions
- PEFT 0.12.0
- Transformers 4.43.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
- Library
- peft
- License
- mit
- Created
- Aug 3, 2024
- Updated
- Aug 3, 2024