Create README.md (#1)

- Create README.md (eb0849e4c2205deaf7d381ecee8cee956f68d8b5)


Co-authored-by: Álvaro Pérez Pozo <alvp@users.noreply.huggingface.co>

README.md

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+---
+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)