File size: 1,920 Bytes
eb0849e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 |
---
license: apache-2.0
datasets:
- fistro/gromenauer
language:
- es
pipeline_tag: text-generation
---
# Bertin-Gromenauer
<div align=center>
<img alt="BERTIN-gromenauer logo" src="https://huggingface.co/bertin-project/bertin-gromenauer/resolve/main/images/gromenauer.png" width="200px">
</div>
## Overview
Bertin-Gromenauer is a Spanish language model designed to understand and generate high-quality Spanish text. Developed using the robust Mistral architecture, this model has been trained on an extensive literary corpus, ensuring it captures a wide range of linguistic nuances, styles, and contexts found in Spanish literature.
## Model Details
- **Model Type**: Mistral
- **Sequence Length**: 8192
- **Hidden Dimension**: 4096
- **Intermediate Dimension**: 14336
- **Number of Layers**: 32
- **Number of Attention Heads**: 32
- **Number of Key-Value Heads**: 8
- **Activation Function**: SiLU
- **Initializer Range**: 0.02
- **Layer Norm Epsilon**: 1.0e-05
- **Use Flash Attention**: Yes
- **Gradient Checkpointing**: Enabled (Block Size: 5)
- **Sliding Window Attention**: 4096
- **Use Bias**: No
## Training Details
- **Tokenizer**: [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
- **Batch Size**: 512
- **Learning Rate**: 1e-5
- **Optimizer**: Adam with beta1=0.9, beta2=0.95, epsilon=1e-8
- **Weight Decay**: 0.1
- **Warmup Steps**: 200
- **Learning Rate Schedule**: Cosine
- **Number of Training Steps**: 7000
## Usage
To load the model in your project, you can use the following code:
```python
from transformers import AutoModel, AutoTokenizer
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("bertin-project/bertin-gromenauer")
# Load the model
model = AutoModel.from_pretrained("bertin-project/bertin-gromenauer")
# Example usage
text = "Introduce aquí tu texto en español."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs) |