---
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 Description

El modelo es el resultado de un proceso de ajuste fino de [phi-2](https://huggingface.co/microsoft/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:
```
config = LoraConfig(
    r=16,
    lora_alpha=32,
    target_modules=[
    'q_proj',
    'k_proj',
    'v_proj',
    'dense',
    'fc1',
    'fc2',
    ], #print(model) will show the modules to use
    bias="none",
    lora_dropout=0.05,
    task_type="CAUSAL_LM",
```

## Parámetros de entrenamiento
Para el entrenamiento se utilizaron los siguientes parámetros:
```
training_args = TrainingArguments(
    output_dir='./results', 
    overwrite_output_dir=True, 
    per_device_train_batch_size=2,  
    per_device_eval_batch_size=2,  
    gradient_accumulation_steps=5, 
    gradient_checkpointing=True,   
    gradient_checkpointing_kwargs={"use_reentrant": False},
    warmup_steps=50,  
    #max_steps=1000,  
    num_train_epochs=2, 
    learning_rate=5e-5,  
    weight_decay=0.01, 
    optim="paged_adamw_8bit", 
    fp16=True, 
    logging_dir='./logs',
    logging_strategy="steps",
    logging_steps=100,
    save_strategy="steps",
    save_steps=200,
    save_total_limit=2, 
    evaluation_strategy="steps",
    eval_steps=200,
    load_best_model_at_end=True, 
)
```
## 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:** [Sergio Chicón](https://huggingface.co/telodigoensergio)
- **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:** [Microsoft/phi-2](https://huggingface.co/microsoft/phi-2)

### Model Sources

- **Repository:** [Google Colab](https://colab.research.google.com/drive/1qSOtPtRHCN5D1VW6MG-pe17OUZf4D2Q5?usp=sharing)


## Uses

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### Direct Use

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## How to Get Started with the Model

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## Training Details

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#### Summary



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## Environmental Impact

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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).

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## Technical Specifications [optional]

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