---
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.1
tags:
- generated_from_trainer
datasets:
- openwebtext
model-index:
- name: sparse_sparse_80_percent_pretraining_warmup_20K_steps_5k
  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. -->

# sparse_sparse_80_percent_pretraining_warmup_20K_steps_5k

This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on the openwebtext dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7590

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 0
- distributed_type: multi-GPU
- num_devices: 6
- gradient_accumulation_steps: 2
- total_train_batch_size: 96
- total_eval_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 5000

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2712        | 0.05  | 50   | 1.2374          |
| 1.0533        | 0.1   | 100  | 1.0529          |
| 0.9603        | 0.15  | 150  | 0.9668          |
| 0.9102        | 0.19  | 200  | 0.9145          |
| 0.8754        | 0.24  | 250  | 0.8775          |
| 0.8514        | 0.29  | 300  | 0.8503          |
| 0.8417        | 0.34  | 350  | 0.8298          |
| 0.8217        | 0.39  | 400  | 0.8146          |
| 0.8019        | 0.44  | 450  | 0.8026          |
| 0.7902        | 0.48  | 500  | 0.7914          |
| 0.7856        | 0.53  | 550  | 0.7819          |
| 0.7599        | 0.58  | 600  | 0.7734          |
| 0.7646        | 0.63  | 650  | 0.7689          |
| 0.7542        | 0.68  | 700  | 0.7635          |
| 0.7529        | 0.73  | 750  | 0.7581          |
| 0.7594        | 0.78  | 800  | 0.7533          |
| 0.7489        | 0.82  | 850  | 0.7493          |
| 0.7494        | 0.87  | 900  | 0.7452          |
| 0.7441        | 0.92  | 950  | 0.7472          |
| 0.7467        | 0.97  | 1000 | 0.7442          |
| 0.728         | 1.02  | 1050 | 0.7413          |
| 0.7263        | 1.07  | 1100 | 0.7384          |
| 0.7206        | 1.11  | 1150 | 0.7362          |
| 0.7223        | 1.16  | 1200 | 0.7343          |
| 0.7362        | 1.21  | 1250 | 0.7421          |
| 0.7374        | 1.26  | 1300 | 0.7401          |
| 0.7284        | 1.31  | 1350 | 0.7378          |
| 0.7309        | 1.36  | 1400 | 0.7356          |
| 0.724         | 1.41  | 1450 | 0.7339          |
| 0.72          | 1.45  | 1500 | 0.7317          |
| 0.73          | 1.5   | 1550 | 0.7509          |
| 0.7464        | 1.55  | 1600 | 0.7489          |
| 0.742         | 1.6   | 1650 | 0.7461          |
| 0.7378        | 1.65  | 1700 | 0.7447          |
| 0.7328        | 1.7   | 1750 | 0.7433          |
| 0.7433        | 1.75  | 1800 | 0.7411          |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0