README.md

---
library_name: transformers
datasets:
- telodigoensergio/lc-gpt3.5
language:
- es
---

# Model Card for Model ID

Este modelo es el primer paso hacia un modelo de lenguaje que pueda usarse para reescribir de textos de carácter adminsitrativo 
con el objetivo de mejorar su comprensión para personas con alto y bajo nivel cultural y socieconómico. 



## Model Details

### Model Description

El modelo es el resultado de un proceso de ajuste fino de phi-2, desarrollado por microsoft con unos 2.5b de parámetros. Para el
ajuste se han extraído multitud de textos de índole administrativa de las principales páginas web de la administración del Estado español. 

Para la carga y ajuste del modelo se han utilizado técnicas de cuantización con la siguiente configuración: 

'''
bnb_config = BitsAndBytesConfig(load_in_4bit=True,
                                bnb_4bit_quant_type='nf4',
                                bnb_4bit_compute_dtype='float16',
                                bnb_4bit_use_double_quant=True)




'''

y se ha aplicado LoRA a las capas lineales para el fine-tunning. 


## Prompting
El prompt para el uso sigue la siguiente estructura:

prompt = f"""###System:
Lee el siguiente texto y hazlo más claro:
###Texto:

{texto}

###Texto aclarado:
"""




- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

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### Training Procedure 

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

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**APA:**

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## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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