---
base_model: google/gemma-2-2b-it
library_name: peft
license: apache-2.0
language:
- es
tags:
- news
- chat
- LoRa
- conversational AI
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

Lightweight finetuning of google/gemma-2-2b-it on a public dataset of news from Spanish digital newspapers (https://www.kaggle.com/datasets/josemamuiz/noticias-laraznpblico/).

## Model Details

### Model Description

This model is fine-tuned using LoRa (Low-Rank Adaptation) on the "Noticias La Razón y Público" dataset, a collection of Spanish news articles. The finetuning was done with lightweight methods to ensure efficient training while maintaining performance on the news-related language generation tasks.

- **Developed by:** https://talkingtochatbots.com
- **Language(s) (NLP):** Spanish (es)
- **License:** apache-2.0
- **Finetuned from model:** google/gemma-2-2b-it

### Model Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

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### Direct Use

This model can be used for **conversational AI tasks** related to Spanish-language news. The fine-tuned LoRa model is especially suitable for use cases that require both understanding and generating text, such as chat-based interactions, answering questions about news, and discussing headlines.

Copy the code from this Gist for easy chating using Jupyter Notebook: https://gist.github.com/reddgr/20c2e3ea205d1fedfdc8be94dc5c1237

### 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

Copy the code from this Gist for easy chating using Jupyter Notebook: https://gist.github.com/reddgr/20c2e3ea205d1fedfdc8be94dc5c1237

Additionally, you can use the code below to get started with the model.

!python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

save_directory = "./fine_tuned_model"
tokenizer = AutoTokenizer.from_pretrained(save_directory)
model = AutoModelForCausalLM.from_pretrained(save_directory)
peft_model = PeftModel.from_pretrained(model, save_directory)

input_text = "¿Qué opinas de las noticias recientes sobre la economía?"
inputs = tokenizer(input_text, return_tensors="pt")
output = peft_model.generate(**inputs, max_length=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))

## 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

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## 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]

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**APA:**

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## Glossary [optional]

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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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### Framework versions

- PEFT 0.12.0