| --- |
| base_model: mistralai/Mistral-Nemo-Instruct-2407 |
| library_name: peft |
| license: other |
| tags: |
| - llama-factory |
| - lora |
| - generated_from_trainer |
| model-index: |
| - name: heat_transfer_sft_10000_mcq_u_1epoch |
| 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. --> |
|
|
| # heat_transfer_sft_10000_mcq_u_1epoch |
|
|
| This model is a fine-tuned version of [mistralai/Mistral-Nemo-Instruct-2407](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407) on the heat_transfer_10000_mcq_u dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0018 |
|
|
| ## 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: 10 |
| - eval_batch_size: 10 |
| - seed: 42 |
| - distributed_type: multi-GPU |
| - num_devices: 2 |
| - total_train_batch_size: 20 |
| - total_eval_batch_size: 20 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: cosine |
| - num_epochs: 1 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | |
| |:-------------:|:------:|:----:|:---------------:| |
| | 0.0076 | 0.1139 | 50 | 0.0059 | |
| | 0.0054 | 0.2278 | 100 | 0.0050 | |
| | 0.0041 | 0.3417 | 150 | 0.0036 | |
| | 0.0026 | 0.4556 | 200 | 0.0025 | |
| | 0.0025 | 0.5695 | 250 | 0.0024 | |
| | 0.0022 | 0.6834 | 300 | 0.0021 | |
| | 0.0022 | 0.7973 | 350 | 0.0019 | |
| | 0.0021 | 0.9112 | 400 | 0.0018 | |
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| ### Framework versions |
|
|
| - PEFT 0.12.0 |
| - Transformers 4.46.0 |
| - Pytorch 2.4.0+cu121 |
| - Datasets 2.21.0 |
| - Tokenizers 0.20.1 |
