MLOps: Optimización PEFT para Resumen de Diálogos con Flan-T5 (LoRA) - Español
Aprende a optimizar modelos Flan-T5 con PEFT (Lora) para resumir diálogos eficientemente, incluyendo técnicas de prompt tuning y evaluación de resultados.
LLMOps: Prompt Tuning for Dialog Summarization with Flan-T5 in Spanish
Aprende a personalizar y evaluar modelos Flan-T5 para resumen de documentos mediante prompt tuning, con enfoque práctico en español.
Fine-Tuning T5 for Document Classification - Part 0: Proof of Concept (Español)
Personaliza Flan-T5 para clasificación de documentos usando un dataset pequeño de 1000 documentos en 10 clases.
MLOps MLFlow: Fine-tuning Flan-T5 for Sequence-to-Sequence Classification in Spanish
Personaliza un modelo T5 para clasificación de documentos utilizando Sequence to Sequence y MLflow, mejorando tus habilidades en MLOps y aprendizaje automático.
Fine-tuning Flan-T5 for Text Classification Using MLFlow - Spanish Tutorial
Personaliza un modelo T5 para clasificación de documentos utilizando SequenceClassification y MLflow, mejorando tus habilidades en ciencia de datos y aprendizaje automático.
Cost Analysis for Fine-Tuning FLAN-T5 Large Language Models
Discover practical cost estimates and expert insights for fine-tuning FLAN-T5 language models, with guidance on cloud infrastructure optimization and budget management for different model sizes.
Fine-tuning Flan-T5 LLM with HuggingFace Accelerate - Tutorial
Learn how to fine-tune Flan-T5 LLM using HuggingFace Accelerate for multi-GPU environments, with step-by-step guidance on preprocessing and implementation for specific downstream tasks.
Fine-tuning FLAN-T5 Models for Text Summarization with PyTorch
Master Flan-T5 model fine-tuning for text summarization through hands-on PyTorch implementation, complete with practical code examples and real-time demonstrations.
ChatGPT vs Flan-T5: Comparing Proprietary and Free LLMs with Performance Tuning
Explore the differences between ChatGPT and Flan-T5 LLM models, comparing their capabilities, accessibility, and performance optimization through practical demonstrations and hyperparameter tuning techniques.
Running Flan-T5-XL Model on Google Colab - A Free Self-Explaining Language Model
Discover how to implement and optimize the Flan-T5-XL language model on Google Colab, exploring its capabilities in reasoning, essay writing, and translation tasks while working within free resource constraints.
Building a FLAN-T5 XL Model GUI with Gradio in Google Colab - A Step-by-Step Tutorial
Learn to build a Gradio GUI for FLAN-T5-XL model on Google Colab, enabling free text generation, summarization, and translation capabilities with minimal computational resources and step-by-step implementation.
Uncertainty, Prompting, and Chain-of-Thoughts in Large Language Models - Part 2
Dive into advanced AI concepts including temperature scaling, Bayesian calibration, prompt-based finetuning, and chain-of-thought techniques for more reliable and explainable language models.
Building a BLIP-2 Application: Vision Transformer and Language Model Integration
Master building a multimodal AI system that combines Vision Transformers and language models to analyze images and generate contextual responses through practical implementation of BLIP-2 architecture.
How to Fine-tune T5 and Flan-T5 Large Language Models - Theory Introduction
Discover the fundamentals of fine-tuning T5 and Flan-T5 language models, exploring key differences, implementation steps, and practical applications using Hugging Face transformers.
Fine-tuning Flan-T5 for Sequence-to-Sequence Classification with MLFlow
Fine-tune Flan-T5 for sequence-to-sequence classification using MLflow. Learn advanced MLOps techniques for optimizing language models in data science projects.
Fine-tuning Flan-T5 for Text Classification with MLFlow
Aprende a personalizar el modelo T5 base para clasificación de secuencias usando tu propio conjunto de datos y MLflow para MLOps.
MLOps: OpenVino Quantized Pipeline for Grammatical Error Correction
Build a grammar error correction model using OpenVino, combining Roberta-based error detection and Flan-T5 correction, with quantization for efficient CPU inference.