| --- |
| license: afl-3.0 |
| library_name: peft |
| tags: |
| - trl |
| - sft |
| - generated_from_trainer |
| base_model: Viet-Mistral/Vistral-7B-Chat |
| datasets: |
| - generator |
| metrics: |
| - rouge |
| model-index: |
| - name: Vistral_Function_Calling_500 |
| 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. --> |
|
|
| # Vistral_Function_Calling_500 |
| |
| This model is a fine-tuned version of [Viet-Mistral/Vistral-7B-Chat](https://huggingface.co/Viet-Mistral/Vistral-7B-Chat) on the generator dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.2455 |
| - Rouge1: 0.8798 |
| - Rouge2: 0.7704 |
| - Rougel: 0.8144 |
| - Rougelsum: 0.873 |
| - Gen Len: 2048.0 |
| |
| It achieves the following results on the test set: |
| - Loss: 0.2639 |
| - Rouge1: 0.8874 |
| - Rouge2: 0.7745 |
| - Rougel: 0.8141 |
| - Rougelsum: 0.8811 |
| - Gen Len: 2048.0 |
| |
| ## 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: 3 |
| - eval_batch_size: 2 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 6 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_ratio: 0.03 |
| - num_epochs: 2 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
| | 0.8125 | 0.25 | 3 | 0.8199 | 0.8499 | 0.7054 | 0.7623 | 0.8429 | 2048.0 | |
| | 0.658 | 0.5 | 6 | 0.3952 | 0.8634 | 0.7361 | 0.7854 | 0.8564 | 2048.0 | |
| | 0.4082 | 0.75 | 9 | 0.3261 | 0.8732 | 0.7452 | 0.7927 | 0.8657 | 2048.0 | |
| | 0.3302 | 1.0 | 12 | 0.2928 | 0.8733 | 0.7552 | 0.801 | 0.8666 | 2048.0 | |
| | 0.2653 | 1.25 | 15 | 0.2653 | 0.8775 | 0.7646 | 0.809 | 0.8703 | 2048.0 | |
| | 0.2605 | 1.5 | 18 | 0.2528 | 0.8778 | 0.7678 | 0.8119 | 0.8707 | 2048.0 | |
| | 0.2444 | 1.75 | 21 | 0.2476 | 0.8793 | 0.7697 | 0.8132 | 0.872 | 2048.0 | |
| | 0.23 | 2.0 | 24 | 0.2455 | 0.8798 | 0.7704 | 0.8144 | 0.873 | 2048.0 | |
| |
| |
| ### Framework versions |
| |
| - PEFT 0.11.1 |
| - Transformers 4.41.1 |
| - Pytorch 2.3.0+cu121 |
| - Datasets 2.19.2 |
| - Tokenizers 0.19.1 |
