A 1-hour presentation on the LLM development cycle, covering architecture, training, finetuning, and evaluation methods.
Explores methods for using and finetuning pretrained large language models, including feature-based approaches and parameter updates.
A summary of February 2024 AI research, covering new open-source LLMs like OLMo and Gemma, and a study on small, fine-tuned models for text summarization.
A guide to implementing LoRA and the new DoRA method for efficient model finetuning in PyTorch from scratch.
Strategies for improving LLM performance through dataset-centric fine-tuning, focusing on instruction datasets rather than model architecture changes.
A guide to participating in the NeurIPS 2023 LLM Efficiency Challenge, focusing on efficient fine-tuning of large language models on a single GPU.
Guide to finetuning large language models on a single GPU using gradient accumulation to overcome memory limitations.
