---
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_f_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_f_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_f dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0006

## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.0024        | 0.1111 | 50   | 0.0013          |
| 0.0011        | 0.2222 | 100  | 0.0010          |
| 0.0009        | 0.3333 | 150  | 0.0008          |
| 0.001         | 0.4444 | 200  | 0.0008          |
| 0.0009        | 0.5556 | 250  | 0.0008          |
| 0.0006        | 0.6667 | 300  | 0.0007          |
| 0.0007        | 0.7778 | 350  | 0.0007          |
| 0.0007        | 0.8889 | 400  | 0.0006          |
| 0.0009        | 1.0    | 450  | 0.0006          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.46.0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.20.1