JEM-LGU-1

Model Card for Model ID

LGU-focused Filipino-Tagalog/English with RAG implementation

Model Details

Model Description

This model is Filipino-Tagalog/English QLoRA SFT on PLTAT's Filipino LoRA fine-tune. An adapter-on-adapter methodology was used, trained on manually-scraped Philippine public service-related data. This repository also features a notebook that includes the training dataset preprocessing pipeline and an RAG implemenatation.

• Developed by: Ajelmar Medina, Kirk Yendell Marcos, Francine Hernandez, Javin Amanda Beatrix Argueza
• Model type: LoRA Fine-Tuned LLM
• Language(s) (NLP): Filipino, Tagalog, English
• License: Apache-2.0 license
• Finetuned from model: PLTAT/filipino_llama_3.1_FT_lora

Model Sources

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• Repository: https://github.com/ajelmarmedina/JEM_LGU-Filipino_English-Llama3_1-LoRA_FT-RAG
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• Demo: [More Information Needed]

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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How to Get Started with the Model

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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

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Environmental Impact

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

• Hardware Type: T4 GPU (1x)
• Hours used:: 0:31 Hours
• Cloud Provider: Kaggle
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Technical Specifications [optional]

Model Architecture and Objective

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Software

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Citation [optional]

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Glossary [optional]

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Framework versions

• PEFT 0.18.1