llama2-pubmed-lora

Model Card for Model ID

• Modelo fine-tuned del LLaMA 2 7B Chat para tareas de clasificación temática de artículos científicos de PubMed.. --> Entrenado sobre abstracts de investigación biomédica relacionados con:
• Deep Learning
• Covid 19
• Virtual Reality
• Brain-Machine Interfaces
• Human Connectome

Este modelo aprende a reconocer el tema principal de un resumen científico y devolver el nombre de la categoría correspondiente.

Model Details

• Base model: meta-llama/Llama-2-7b-chat-hf
• Adapter type: LoRA (PEFT)
• Framework: Transformers + PEFT
• Language: Inglés (abstracts científicos)
• Fine-tuning type: Instruction-based classification
• License: Meta LLaMA 2 Community License
• Finetuned by: rubix
• Shared on Hugging Face: rubendc22

Model Description

Este modelo está diseñado para analizar y clasificar abstracts científicos en categorías temáticas.
El fine-tuning se realizó utilizando un dataset balanceado y limpiado de PubMed Abstracts (Bonhart), con formato instruccional.

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Uses

Clasificación de artículos científicos en temáticas dadas

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

El modelo está entrenado solo para abstracts científicos en inglés.

No debe usarse para diagnóstico médico real.

Los resultados dependen del contexto textual (abstracts muy cortos o ambiguos pueden clasificarse erróneamente).

Posibles sesgos inherentes al dataset de PubMed (centrado en investigaciones biomédicas anglosajonas).

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Recommendations

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How to Get Started with the Model

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Training Details

Dataset: bonhart/pubmed-abstracts (Kaggle)

• Tamaño del dataset: ~13.000 abstracts
• Preprocesamiento: limpieza, deduplicación y balanceo
• Entrenamiento: fine-tuning con formato instructivo (input + output)
• Batch size: 2
• Learning rate: 2e-5
• Epochs: 2
• Precision: FP16
• Hardware: NVIDIA T4 (Google Colab)
• Método: LoRA (low-rank adaptation)
• Framework: Transformers + PEFT

Training Data

en proceso de mejora

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Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

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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

Hardware: GPU T4 (Google Colab)

Tiempo de entrenamiento: ~1.5 h

Energía estimada: ~0.3 kWh

Emisiones estimadas: < 0.15 kg CO₂eq

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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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

@misc{llama2_pubmed_instruct, author = {rubendc22}, title = {LLaMA2-PubMed-Instruct: Fine-tuned LLaMA 2 7B Chat model for scientific text classification}, year = {2025}, howpublished = {\url{https://huggingface.co/tu_usuario/llama2-pubmed-instruct}} }

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Framework versions

• PEFT 0.17.1