The article discusses fine-tuning large language models (LLMs) using QLoRA with different quantization methods, including AutoRound, AQLM, GPTQ, AWQ, and bitsandbytes. It compares their performance and speed, recommending AutoRound for its balance of quality and speed.
This article provides a comprehensive guide on fine-tuning the Llama 3.1 language model using Unsloth for efficient parameter-efficient training. It covers concepts like supervised fine-tuning, LoRA, QLoRA, and practical steps for training on a high-quality dataset.
This article announces a comprehensive course on fine-tuning large language models (LLMs) offered on the freeCodeCamp.org YouTube channel. The course, developed by Krish Naik, covers topics such as QLORA, LORA, quantization with LLama2, gradient, and Google Gemma Model, among others. The course aims to help learners deepen their understanding of machine learning and artificial intelligence.
efficient method for fine-tuning LLM using LoRA and QLoRA, making it possible to train them even on consumer hardware
Details:
IS-LM 3B is StableLM 3B 4E1T(Licensed under CC BY-SA 4.0.) instruction tuned on DataForge Economics for 3 epochs with QLoRA(2305.14314).
IS-LM 3B is StableLM 3B 4E1T(Licensed under CC BY-SA 4.0.) instruction tuned on DataForge Economics for 3 epochs with QLoRA(2305.14314).
