| | --- |
| | 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-embedding-concat |
| | 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-embedding-concat |
| |
|
| | 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: 0.5088 |
| |
|
| | ## 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: 2 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | |
| | |:-------------:|:------:|:----:|:---------------:| |
| | | 0.6816 | 0.2256 | 50 | 0.6683 | |
| | | 0.5538 | 0.4512 | 100 | 0.5632 | |
| | | 0.53 | 0.6768 | 150 | 0.5379 | |
| | | 0.5764 | 0.9024 | 200 | 0.5253 | |
| | | 0.5071 | 1.1280 | 250 | 0.5177 | |
| | | 0.4961 | 1.3536 | 300 | 0.5132 | |
| | | 0.4674 | 1.5792 | 350 | 0.5103 | |
| | | 0.5158 | 1.8049 | 400 | 0.5088 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - PEFT 0.12.0 |
| | - Transformers 4.44.2 |
| | - Pytorch 2.4.1+cu121 |
| | - Datasets 3.0.0 |
| | - Tokenizers 0.19.1 |
