---
license: apache-2.0
tags:
- generated_from_trainer
base_model: mistralai/Mistral-7B-Instruct-v0.1
model-index:
- name: mistral_docs_sum_p1_full
  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. -->

# mistral_docs_sum_p1_full

This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5829

## 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: 3.6e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.1167        | 0.0277 | 200  | 2.1333          |
| 2.3428        | 0.0553 | 400  | 1.6966          |
| 1.3784        | 0.0830 | 600  | 1.4972          |
| 1.456         | 0.1107 | 800  | 1.3942          |
| 1.3227        | 0.1383 | 1000 | 1.3084          |
| 1.2535        | 0.1660 | 1200 | 1.2001          |
| 1.0612        | 0.1937 | 1400 | 1.0451          |
| 0.8815        | 0.2213 | 1600 | 0.9632          |
| 0.8971        | 0.2490 | 1800 | 0.9132          |
| 0.7908        | 0.2767 | 2000 | 0.8712          |
| 0.7549        | 0.3043 | 2200 | 0.8309          |
| 0.8099        | 0.3320 | 2400 | 0.8058          |
| 0.6891        | 0.3597 | 2600 | 0.7879          |
| 0.5204        | 0.3873 | 2800 | 0.7684          |
| 0.6249        | 0.4150 | 3000 | 0.7515          |
| 0.6764        | 0.4427 | 3200 | 0.7342          |
| 0.6996        | 0.4703 | 3400 | 0.7214          |
| 0.6371        | 0.4980 | 3600 | 0.7084          |
| 0.6694        | 0.5257 | 3800 | 0.6951          |
| 0.7048        | 0.5533 | 4000 | 0.6845          |
| 0.7265        | 0.5810 | 4200 | 0.6778          |
| 0.5663        | 0.6087 | 4400 | 0.6657          |
| 0.6222        | 0.6363 | 4600 | 0.6595          |
| 0.6463        | 0.6640 | 4800 | 0.6488          |
| 0.5754        | 0.6917 | 5000 | 0.6410          |
| 0.6208        | 0.7193 | 5200 | 0.6363          |
| 0.5613        | 0.7470 | 5400 | 0.6275          |
| 0.6316        | 0.7747 | 5600 | 0.6227          |
| 0.6564        | 0.8023 | 5800 | 0.6159          |
| 0.633         | 0.8300 | 6000 | 0.6077          |
| 0.5268        | 0.8577 | 6200 | 0.6022          |
| 0.4166        | 0.8853 | 6400 | 0.5978          |
| 0.6539        | 0.9130 | 6600 | 0.5926          |
| 0.5695        | 0.9407 | 6800 | 0.5875          |
| 0.6358        | 0.9683 | 7000 | 0.5845          |
| 0.5318        | 0.9960 | 7200 | 0.5829          |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1