Fine-tuning Phi-3 for LeetCode: Dataset Generation and Unsloth Implementation
Learn to generate LeetCode datasets and fine-tune Phi-3 models using Unsloth for improved coding problem-solving performance. Explore dataset creation, model training, and result evaluation.
Generative AI: Fine-Tuning LLM Models Crash Course
Comprehensive guide to fine-tuning LLMs using advanced techniques like QLORA and quantization, covering theoretical concepts and practical implementations with popular models like LLama2 and Google Gemma.
Leveraging LLMs for Enterprise Data Management - Strategies and Applications
Explore essential LLM strategies for non-public data applications, including prompt engineering, fine-tuning, and Retrieval Augmented Generation. Learn to transform enterprise data processes with practical techniques and real-world examples.
Building the Fine-Tuning Pipeline for Alignment of LLMs
Explore key aspects of aligning LLMs and set up infrastructure for a versatile alignment pipeline. Learn data collection, prompt tuning, and workflow management techniques.
Fine-tuning LLMs with Hugging Face SFT and QLoRA - LLMOps Techniques
Explore advanced LLM fine-tuning techniques, from specialized tuning to instruction enhancement. Learn dataset preparation, model optimization, and leverage powerful libraries for efficient fine-tuning and deployment.
Building LLM-Powered Apps: Best Practices for Vector Databases and LLMOps
Explore best practices for LLM-based app development, covering design considerations, technical aspects, and deployment strategies for optimal performance and scalability.
Microsoft Phi3 Mini 4K Instruct - Chain of Thought Prompting Test
Explore Microsoft Phi3 Mini 4K's Chain of Thought prompting capabilities through practical tests, comparing results with other models like LLama2, Mistral 7B, and LLama3 8B.
Llama2 7B QLora Fine Tuning with Chain-of-Thought Dataset
Fine-tune Llama2 7B model using QLora with chain-of-thought dataset. Learn step-by-step process, access provided notebooks, and explore CodeLlama implementation for enhanced machine learning capabilities.
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 with MLflow: Fine-Tuning LLMs - Tracking Experiments and Logging Models
Fine-tune LLMs, track experiments, and log models using MLflow for efficient MLOps practices in data science and machine learning projects.
Llama3 8B QLora Fine-Tuning with Chain-of-Thought Dataset
Fine-tune Llama3 8B model using QLora with chain-of-thought dataset. Learn step-by-step process and access provided notebooks for Llama3, Llama2, and Mistral models.
LLMOps: Converting Fine-Tuned ViT Classifier to ONNX for CPU Inference
Convert a fine-tuned ViT Transformer to ONNX format and perform CPU inference, enhancing model portability and efficiency for custom image classification tasks.
LLMOps: Fine-Tuning Video Classifier (ViViT) with Custom Data
Fine-tune a Video Vision Transformer (ViViT) using custom datasets. Learn to adapt Google's pretrained model for specialized video classification tasks.
Fine-Tuning ViT Classifier with Retina Images and Converting to ONNX - Video 3
Fine-tune a Vision Transformer for diabetic retinopathy detection using EyeQ dataset and convert the model to ONNX format for efficient deployment.
Divide and Conquer: Carving Out Symbolic Models from BlackBox for More Efficient Domain Adaptation
Explore techniques for extracting symbolic models from black-box systems, enhancing domain adaptation efficiency in computational genomics and medical imaging.
What Is ChatGPT Doing? Training Neural Networks - Episode 4
Explore the inner workings of ChatGPT and large language models, covering neural network training, fine-tuning, reinforcement learning, and parameter optimization in this insightful discussion.
Why My Students Design Escape Rooms
Discover how designing escape rooms enhances game design skills like brainstorming, prototyping, and iteration. Learn innovative teaching methods for developing creative problem-solving abilities in students.
Character Control with Neural Networks and Machine Learning
Explore data-driven animation systems using neural networks and machine learning to simplify character control in game development, reducing complexity and resource requirements.
Fine-Tuning Giant Neural Networks on Commodity Hardware with Automatic Pipeline Model Parallelism
Explore fine-tuning giant neural networks on commodity hardware using FTPipe, a system enabling multi-GPU execution through innovative model partitioning and task allocation techniques.
Why Dolly Is Just the Beginning for Open LLM Models
Explore the evolution of open LLMs, focusing on Dolly's impact and the potential for fine-tuning smaller models with proprietary data to gain a competitive edge in AI development.