Downloads · 30 days
6
29% of all-time downloads
Phi-3.5-MultiCap-tool
sofyc/Phi-3.5-MultiCap-tool
Phi-3.5-MultiCap-tool is a machine learning model from sofyc. 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. --
All-time downloads
21
Public
Repo size
36.2 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors35.7 MB · 94%
At a glance
- Library
- peft
- License
- mit
- Access
- Public
- Created
- Sep 10, 2024
- Updated
- Sep 10, 2024
- SHA
- 46d27ade
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-3.5-MultiCap-tool
This model is a fine-tuned version of microsoft/Phi-3.5-mini-instruct on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4301
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: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.9379 | 0.2256 | 50 | 0.9483 |
| 0.5752 | 0.4512 | 100 | 0.5681 |
| 0.5014 | 0.6768 | 150 | 0.4917 |
| 0.5155 | 0.9024 | 200 | 0.4731 |
| 0.4501 | 1.1280 | 250 | 0.4633 |
| 0.4451 | 1.3536 | 300 | 0.4565 |
| 0.4214 | 1.5792 | 350 | 0.4517 |
| 0.4795 | 1.8049 | 400 | 0.4477 |
| 0.4448 | 2.0305 | 450 | 0.4447 |
| 0.4181 | 2.2561 | 500 | 0.4423 |
| 0.4353 | 2.4817 | 550 | 0.4403 |
| 0.4613 | 2.7073 | 600 | 0.4386 |
| 0.4573 | 2.9329 | 650 | 0.4371 |
| 0.4719 | 3.1585 | 700 | 0.4362 |
| 0.4174 | 3.3841 | 750 | 0.4347 |
| 0.4337 | 3.6097 | 800 | 0.4340 |
| 0.4478 | 3.8353 | 850 | 0.4332 |
| 0.4156 | 4.0609 | 900 | 0.4325 |
| 0.4177 | 4.2865 | 950 | 0.4318 |
| 0.4113 | 4.5121 | 1000 | 0.4315 |
| 0.4343 | 4.7377 | 1050 | 0.4311 |
| 0.423 | 4.9633 | 1100 | 0.4307 |
| 0.4492 | 5.1889 | 1150 | 0.4307 |
| 0.4417 | 5.4146 | 1200 | 0.4303 |
| 0.4485 | 5.6402 | 1250 | 0.4302 |
| 0.4374 | 5.8658 | 1300 | 0.4301 |
Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
- Library
- peft
- License
- mit
- Created
- Sep 10, 2024
- Updated
- Sep 10, 2024