klotz: mistral 7b* + llm*

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  1. This tutorial guides readers on how to fine-tune the Mistral 7B large language model using QLoRA with the Axolotl library, focusing on managing limited GPU resources for efficient training. It covers environment setup, dataset creation, configuration of QLoRA hyperparameters, the fine-tuning process, and testing the fine-tuned model.

  2. This paper explores whether some language model representations may be inherently multi-dimensional, contrasting the linear representation hypothesis. The authors develop a method using sparse autoencoders to find multi-dimensional features in GPT-2 and Mistral 7B. They find interpretable examples such as circular features representing days of the week and months of the year, which are used to solve computational problems involving modular arithmetic.

  3. Explore the 14 top open-source Large Language Models (LLMs) available for research and commercial use. These open-source models provide transparency, no vendor lock-in, and total control over customization. This article provides detailed information about each model including parameters, license, and usage.

  4. An in-depth guide about Mistral 7B, a 7-billion-parameter language model released by Mistral AI. This guide includes an introduction to the model, its capabilities, code generation, limitations, guardrails, and enforcing guardrails. It also covers applications, papers, and additional reading materials related to Mistral 7B and finetuned models.

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