Fine-Tuning Large Language Models with Limited Resources - NeurIPS Hacker Cup AI
Strategies for fine-tuning large language models with limited resources: memory optimization, gradient accumulation, activation checkpointing, LoRa, and dataset quality. Practical advice for 40 GB VRAM GPU constraints.
Fine-tuning LLMs to Reduce Hallucination - Leveraging Out-of-Domain Data
Explore techniques to enhance LLM factual consistency using out-of-domain data and two-step fine-tuning. Learn to leverage Weights & Biases for model evaluation and improve hallucination detection.
How to Fine-Tune Your Own Language Model - OpenPipe CEO Kyle Corbitt
Explore fine-tuning language models with OpenPipe's CEO. Learn about model selection, data curation, hyperparameter optimization, and validation processes for efficient AI development and deployment.
Stable Diffusion 3 2B Medium Training with Kohya and SimpleTuner - Full Finetune and LoRA
Comprehensive guide on training Stable Diffusion 3 for art styles using kohya and SimpleTuner, covering setup, parameters, experiments, and result analysis for both full finetune and LoRA approaches.
Full Fine-tuning LLMs with Lower VRAM: Optimizers, GaLore, and Advanced Techniques
Explore advanced techniques for full fine-tuning LLMs with limited GPU resources, including optimizer tricks, Galore, and Adafactor. Enhance model performance and efficiency.
Text-to-Speech Fine-tuning Tutorial - StyleTTS2 Voice Cloning and Model Adaptation
Master text-to-speech fine-tuning techniques, from dataset preparation to model training. Explore voice cloning, StyleTTS2, and performance optimization for advanced speech synthesis projects.
Mastering Retrieval for LLMs - BM25, Fine-tuned Embeddings, and Re-Rankers
Enhance LLM retrieval with advanced techniques: BM25, fine-tuned embeddings, and re-rankers. Learn document chunking, semantic search, and performance optimization for improved AI-powered information retrieval.
Custom RAG Pipelines and LLM Fine-Tuning - A Gradient Tutorial
Explore custom RAG pipelines and LLM fine-tuning using Gradient. Learn to deploy fine-tuned models, mimic Yoda's speech style, and efficiently fine-tune 13B parameter open-source models for unique applications.
Data Preparation Tips and Tricks for Machine Learning
Master data preparation techniques for AI models, including filtering, balancing, and synthetic dataset creation. Explore clustering methods, chat templates, and handling mixed-language data for improved model performance.
LoRA Fine-tuning Explained - Choosing Parameters and Optimizations
Explore LoRA fine-tuning techniques, from parameter selection to optimization strategies, enhancing your ability to customize language models effectively.
The Best Tiny Language Models - Performance, Fine-tuning, and Function-calling
Explore the benefits, performance, and fine-tuning of small language models. Learn about function-calling with quantized models and discover the best tiny LLMs for various applications.
Fine-tuning Language Models for Structured Responses with QLoRa - Lecture
Master fine-tuning language models for structured responses using QLoRa. Learn quantization, data setup, training, inference, and advanced techniques to enhance model performance and efficiency.
Optimizing Large Language Model Selection for Efficient GenAI Development
Discover practical tools and strategies for selecting optimal LLMs in GenAI development, exploring DSPy and frugalGPT frameworks for cost-effective and efficient implementation.
Getting Started with Generative Text and Fine-Tuning LLMs in Hugging Face
Dive into practical LLM implementation using Hugging Face's transformers library, from basic text generation to efficient model fine-tuning techniques for customized applications.
LLM Post-Training - Lecture 14
Dive into advanced LLM post-training techniques and methodologies with Prof. Song Han, exploring efficient machine learning strategies for optimizing large language models.
Pre-training Mixtral MoE Model with SageMaker HyperPod - Fine-Tuning and Continued Pre-Training
Master advanced techniques for pre-training and fine-tuning Mixtral MoE models using AWS SageMaker HyperPod and SLURM, covering implementation strategies and continued pre-training approaches.
From Zero to SOTA: Fine-Tuning BERT Using Hugging Face for World-Class Training Performance
Discover how to fine-tune BERT using Hugging Face and Graphcore's IPU for state-of-the-art NLP performance. Learn to accelerate AI development, optimize models, and leverage cloud platforms for efficient machine learning workflows.
Fine-Tuning Language Models with Declarative ML Orchestration
Gain hands-on experience using Flyte to declaratively specify infrastructure for fine-tuning language models on your own data, overcoming compute resource challenges.
EfficientML.ai: Quantization Part II - Lecture 6
Explore advanced quantization techniques for efficient machine learning, including Rooney quantization, scaling factors, and fine-tuning strategies.
Efficient Fine-tuning and Prompt Engineering - Lecture 20
Explore efficient fine-tuning techniques and prompt engineering strategies for optimizing large language models in machine learning applications.
Efficient Fine-tuning and Prompt Engineering - Lecture 20
Explore efficient fine-tuning techniques and prompt engineering strategies for optimizing large language models in machine learning applications.