transformers safetensors mistral trl sft generated_from_trainer endpoints_compatible text-generation-inference region:us
---
tags:
- trl
- sft
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: mistral_sparse_80_percent_cola_1000
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# mistral_sparse_80_percent_cola_1000

This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3726
- Accuracy: 0.8441

## 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: 2
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4653        | 0.22  | 50   | 0.5302          | 0.7405   |
| 0.5191        | 0.44  | 100  | 0.4846          | 0.7638   |
| 0.5233        | 0.66  | 150  | 0.4720          | 0.7701   |
| 0.4905        | 0.88  | 200  | 0.4463          | 0.7802   |
| 0.3672        | 1.1   | 250  | 0.4354          | 0.7927   |
| 0.3929        | 1.32  | 300  | 0.4171          | 0.8028   |
| 0.3643        | 1.54  | 350  | 0.4110          | 0.7997   |
| 0.324         | 1.76  | 400  | 0.3927          | 0.8231   |
| 0.3639        | 1.98  | 450  | 0.4550          | 0.7747   |
| 0.3293        | 2.2   | 500  | 0.4191          | 0.8309   |
| 0.3072        | 2.42  | 550  | 0.4059          | 0.8184   |
| 0.3131        | 2.64  | 600  | 0.3780          | 0.8363   |
| 0.3821        | 2.86  | 650  | 0.3804          | 0.8301   |
| 0.2741        | 3.08  | 700  | 0.3789          | 0.8394   |
| 0.258         | 3.3   | 750  | 0.3984          | 0.8394   |
| 0.2316        | 3.52  | 800  | 0.3998          | 0.8363   |
| 0.1955        | 3.74  | 850  | 0.3799          | 0.8465   |
| 0.2266        | 3.96  | 900  | 0.3750          | 0.8426   |
| 0.1476        | 4.18  | 950  | 0.4402          | 0.8332   |
| 0.1088        | 4.4   | 1000 | 0.4813          | 0.8316   |
| 0.1872        | 4.62  | 1050 | 0.4342          | 0.8410   |
| 0.1248        | 4.84  | 1100 | 0.4700          | 0.8472   |
| 0.108         | 5.05  | 1150 | 0.4632          | 0.8472   |
| 0.1437        | 5.27  | 1200 | 0.6568          | 0.8387   |


### Framework versions

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