reddgr/gemma-2-2b-ft-lora-noticias-es

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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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/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

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

Preprocessing [optional]

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Training Hyperparameters

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Evaluation

Testing Data, Factors & Metrics

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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

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Hardware

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Software

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Citation [optional]

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Framework versions

• PEFT 0.12.0