BayJarvis: Blogs on mistral

paper Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models - 2024-02-06

A key challenge has been improving these models beyond a certain point, especially without the continuous infusion of human-annotated data. A groundbreaking paper by Zixiang Chen, Yihe Deng, Huizhuo Yuan, Kaixuan Ji, and Quanquan Gu presents an innovative solution: Self-Play Fine-Tuning (SPIN). …

paper Mixture-of-Experts Meets Instruction Tuning: A Winning Combination for Large Language Models - 2023-12-19

This study advocates integrating Sparse Mixture-of-Experts (MoE) architecture with instruction tuning, demonstrating its superiority over traditional dense models. …

paper Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer - 2023-12-18

In the landmark paper "Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer," a revolutionary approach to neural network scalability is unveiled, fundamentally challenging conventional methods in neural network design. This study, spearheaded by Noam Shazeer and his team, introduces a novel strategy to expand the capacity of neural networks significantly, without necessitating a proportional increase in computational resources. At the core of this innovation is the development of the Sparsely-Gated Mixture-of-Experts (MoE) layer, a sophisticated assembly of numerous feed-forward sub-networks known as 'experts', governed by a trainable gating network. …

paper Learning Factored Representations in a Deep Mixture of Experts - 2023-12-15

In the field of machine learning, the Deep Mixture of Experts (DMoE) model, as discussed in "Learning Factored Representations in a Deep Mixture of Experts," offers a novel perspective. To fully appreciate its impact, we must first explore its predecessors: the standard Mixture of Experts (MoE), the Product of Experts (PoE), and the Hierarchical Mixture of Experts. …

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 Unleashing Dual Power: Switching Seamlessly Between Zephyr & Mistral 7B Models in Multiple LLMs - 2023-11-09

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. …

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. …