Model Card for Model ID
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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- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
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/20c2e3ea205d1fedfdc8be94dc5c1237Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
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/20c2e3ea205d1fedfdc8be94dc5c1237Additionally, 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
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Training Procedure
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Evaluation
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Summary
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Environmental Impact
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