BayJarvis: Blogs on quantization

paper BitNet: Scaling 1-bit Transformers for Large Language Models - 2024-03-09

The exponential growth of large language models poses significant challenges in terms of deployment costs and environmental impact due to high energy consumption. In response to these challenges, this paper introduces BitNet, a scalable and stable 1-bit Transformer architecture designed for large language models. By introducing BitLinear as a replacement for the traditional nn.Linear layer, BitNet aims to train with 1-bit weights from scratch, significantly reducing the memory footprint and energy consumption while maintaining competitive performance. …

llm In Brief: Welcome Google's Gemma - New Open LLM - 2024-02-22

Google has just introduced Gemma, an innovative family of state-of-the-art open Large Language Models (LLMs), marking a significant stride in the open-source AI landscape. This release, featuring both 7B and 2B parameter models, underscores Google's ongoing commitment to open-source AI. The Hugging Face team is thrilled to support this launch, ensuring seamless integration within our ecosystem. …

llm Harnessing Zephyr's Breeze: DPO Training on Mistral-7B-GPTQ for Language Model Alignment - 2023-11-09

We've taken on the exciting challenge of implementing the cutting-edge strategies presented in "ZEPHYR: Direct Distillation of LM Alignment". This paper's approach is not just theoretical—it's a blueprint for a significant leap in language model training. By adopting ZEPHYR's distilled direct preference optimization (dDPO), we've embarked on a code journey that brings these innovations from concept to reality. …

llm Fine-tuning Zephyr 7B GPTQ with 4-Bit Quantization for Custom Data and Inference - 2023-11-08

Model fine-tuning and quantization play pivotal roles in creating efficient and robust machine learning solutions. This blog post explores the fine-tuning process of the Zephyr 7B GPT-Q model using 4-bit quantization to boost its performance for custom data inference tasks. …

llm Optimizing Llama 2: Harnessing the Power of Prompt, RAG, and Fine-Tuning - 2023-11-04

In the rapidly evolving landscape of large language models (LLMs), enhancing their capabilities and performance is pivotal. Three prominent techniques that stand out in achieving this are: …

llm Building the Future of Instruction-Based Code Generation: An Exploration of Code Alpaca's LLaMA Models with Ludwig's Fine-Tuning QLORA Technique - 2023-09-01

In the vast realm of machine learning, fine-tuning stands out as one of the most crucial techniques for adapting pre-trained models to new tasks. Ludwig, a deep learning toolkit, offers a diverse palette of fine-tuning strategies that cater to different needs. In this blog, we'll delve into these techniques, especially focusing on the Quantization-Based Fine-Tuning (QLoRA) method, as we explore the Code Alpaca project's efforts in instruction-based code generation using LLaMA models. …

paper Revolutionizing Language Model Fine-Tuning: The Power of QLORA - 2023-08-27

In the AI realm, language models are paramount. From revolutionizing chatbots to pioneering content generation, they've altered our machine interaction landscape. But like all great innovations, challenges persist. As these models burgeon in sophistication, so does their memory appetite, making their pivotal optimization process, fine-tuning, a pricey endeavor. That's where QLORA steps in, heralding a new era for Large Language Models (LLMs). …