---
base_model: mistralai/Mistral-Nemo-Instruct-2407
library_name: peft
license: other
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: combined_sft_10000_mcq_2epoch
  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. -->

# combined_sft_10000_mcq_2epoch

This model is a fine-tuned version of [mistralai/Mistral-Nemo-Instruct-2407](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407) on the combined_10000_mcq dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0011

## 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: 4
- total_train_batch_size: 40
- total_eval_batch_size: 40
- 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: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0044        | 0.0667 | 30   | 0.0043          |
| 0.0039        | 0.1333 | 60   | 0.0039          |
| 0.0039        | 0.2    | 90   | 0.0038          |
| 0.0038        | 0.2667 | 120  | 0.0039          |
| 0.0038        | 0.3333 | 150  | 0.0037          |
| 0.0036        | 0.4    | 180  | 0.0034          |
| 0.0034        | 0.4667 | 210  | 0.0033          |
| 0.0028        | 0.5333 | 240  | 0.0027          |
| 0.0026        | 0.6    | 270  | 0.0024          |
| 0.0022        | 0.6667 | 300  | 0.0022          |
| 0.0025        | 0.7333 | 330  | 0.0020          |
| 0.0019        | 0.8    | 360  | 0.0019          |
| 0.0021        | 0.8667 | 390  | 0.0018          |
| 0.0017        | 0.9333 | 420  | 0.0017          |
| 0.0017        | 1.0    | 450  | 0.0017          |
| 0.0017        | 1.0667 | 480  | 0.0017          |
| 0.0015        | 1.1333 | 510  | 0.0016          |
| 0.0015        | 1.2    | 540  | 0.0016          |
| 0.0014        | 1.2667 | 570  | 0.0016          |
| 0.0017        | 1.3333 | 600  | 0.0014          |
| 0.0014        | 1.4    | 630  | 0.0014          |
| 0.0012        | 1.4667 | 660  | 0.0013          |
| 0.0012        | 1.5333 | 690  | 0.0013          |
| 0.0012        | 1.6    | 720  | 0.0012          |
| 0.0011        | 1.6667 | 750  | 0.0012          |
| 0.0009        | 1.7333 | 780  | 0.0011          |
| 0.0012        | 1.8    | 810  | 0.0011          |
| 0.0012        | 1.8667 | 840  | 0.0011          |
| 0.0012        | 1.9333 | 870  | 0.0011          |
| 0.001         | 2.0    | 900  | 0.0011          |


### Framework versions

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