Finetuning a Large Language Model on a Custom Dataset
Discover parameter-efficient finetuning techniques and optimizations for large language models using custom datasets, enhancing model performance at scale.
IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation - M2.6
Explore efficient multimodal representation adaptation for sequential recommendations using decoupled PEFT in this innovative research presentation.
When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications - Lecture 1
Explore parameter-efficient fine-tuning techniques for multi-task medical applications using Mixture of Experts and Large Language Models.
Fine-Tuning Large Language Models at Scale - Workday's Approach
Dive into Workday's innovative approach to fine-tuning LLMs securely in a multi-tenant environment, addressing GPU scarcity and data access controls using PEFT techniques and KubeRay's autoscaling capabilities.
LoRA - Low Rank Adaptation for Parameter Efficient Fine-Tuning
Dive into parameter-efficient fine-tuning with LoRA, exploring low-rank matrices, adapters, and techniques to optimize neural network storage and performance while maintaining accuracy.
Coding RLHF on LLama 2 with LoRA, 4-bit Quantization, TRL and DPO
Master advanced LLM fine-tuning techniques using RLHF, implementing DPO and PPO methods on Llama models with 4-bit quantization and LoRA for optimized performance and human feedback integration.
Fine-tuning Gemma Models with PEFT and LoRA Using HuggingFace Datasets
Master parameter-efficient fine-tuning techniques for Gemma 2B model using PEFT and LoRA, with hands-on code implementation and custom dataset creation for optimized model training.
Understanding 4-bit Quantization and QLoRA - Memory Efficient Fine-tuning of LLMs
Dive into memory-efficient fine-tuning of large language models through 4-bit quantization and QLoRA, combining theoretical foundations with practical implementation in Colab.
Parameter Efficient Fine-Tuning with Multiple LoRA Adapters for Large Language Models
Dive into parameter efficient fine-tuning with LoRA adapters, exploring matrix factorization, SVD, and advanced configurations for optimizing large language models.
LLama 2 and PEFT Documentation - Building Interactive LLM with Retrieval Augmented Generation
Master building a RAG-enhanced LLM system using Llama 2 through hands-on coding. Learn web scraping, embedding generation, vector indexing, and semantic search to create an AI that leverages external knowledge sources.
Fine-tuning LLama 2 with PEFT, LoRA, 4-bit Quantization, TRL and SFT
Master advanced LLama2 fine-tuning techniques using PEFT, LoRA, and 4-bit quantization while creating synthetic datasets with GPT-4 for customized model training.
Stanford ALPACA 7B LLM: Fine-tuning Guide with Code and Datasets
Discover how to fine-tune and implement Stanford's ALPACA 7B language model using LLaMA, exploring practical applications and cost-effective methods for creating custom AI solutions.
Fine-tuning Vision Language Models with PaLiGemma-3B - A Practical Guide
Master fine-tuning techniques for Vision Language Models using PyTorch and JAX FLAX, with practical examples for PaliGemma-3B implementation on free compute platforms like Google Colab and Vertex AI.
Optimizing LLM Fine-Tuning with PEFT and LoRA Adapter-Tuning for GPU Performance
Master efficient fine-tuning of large language models using PEFT and LoRA techniques, optimizing GPU memory usage through INT8 quantization and adapter-tuning for cost-effective model development.
Parameter-Efficient Fine-Tuning with LoRA - Optimizing LLMs for Local GPU Training
Master PEFT LoRA techniques to efficiently fine-tune large language models on local GPUs through low-rank adaptation, reducing memory requirements while maintaining model performance.
Fine-Tuning Code Language Models - A Practical Guide
Master the process of fine-tuning Code LLMs using instruction datasets, from environment setup to implementation, with hands-on demonstrations using StarCoder and practical GPU requirements guidance.
Modular and Composable Transfer Learning
Explore parameter-efficient transfer learning with adapters, enabling modular and composable fine-tuning for improved task performance and cross-lingual generalization in transformer-based models.
Fine-Tuning LLMs with PEFT and LoRA
Explore efficient fine-tuning of large language models using PEFT and LoRA techniques, with a focus on practical implementation and uploading to HuggingFace Hub.
Building Better Language Models - Paradigms and Techniques
Explore advanced techniques for building and improving language models, covering paradigms, zero-shot learning, and multitask prompted training.
Generative AI and Large Language Models: Fine-tuning with SageMaker, PEFT, RLHF and PPO
Master advanced LLM fine-tuning techniques using Amazon SageMaker, exploring PEFT, RLHF, and PPO methodologies for optimized AI model performance and customization.
Fine-tuning Large Models on Local Hardware Using PEFT and Quantization
Explore Parameter-Efficient Fine-Tuning (PEFT) and quantization techniques to train large neural networks on modest hardware using the PEFT library and Hugging Face ecosystem.