| | --- |
| | base_model: microsoft/Phi-3.5-mini-instruct |
| | library_name: peft |
| | license: mit |
| | tags: |
| | - trl |
| | - sft |
| | - generated_from_trainer |
| | model-index: |
| | - name: Phi-3.5-MultiCap-tool |
| | results: [] |
| | --- |
| | |
| | <!-- 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](https://huggingface.co/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 |
