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. …
The application of Socratic methods to LLMs like GPT-4 can significantly enhance their ability to process and interpret complex inquiries. Here's how some of these prompt templates can be applied: …
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. …
In today's rapidly growing world of conversational AI, developers often seek ways to leverage multiple models seamlessly to diversify outputs and enhance user experience. One such scenario involves using different Local Language Models (LLMs) to serve different purposes or to offer a variety of responses. In this article, we'll explore a method to set up and switch between multiple local LLMs, particularly Zephyr and Mistral 7B, using the Chainlit and Langchain libraries. …
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. …
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: …
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. …
Machine learning enthusiasts and researchers are constantly advancing the frontiers of technology, crafting larger and more sophisticated models, especially in the domain of Natural Language Processing (NLP). However, not all of us have the resources to run these behemoths. If you've ever been curious about running the more manageable, smaller counterparts of some of the most prominent language models on your own computer, then this blog post offers the perfect insight! …