fine-tunning for Biologie task.

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 3.0

### Training results



### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.7
- Tokenizers 0.14.1
{"language": ["en"], "license": "mit", "tags": ["biology"], "datasets": ["A2H0H0R1/Animal-nutrition"], "tensor type": ["fp16"]}
transformers pytorch mistral text-generation biology en dataset:A2H0H0R1/Animal-nutrition license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us