GGUF stands for "Generalized GPT-Unified Format". It is a new format for storing and loading large language models (LLMs) that was introduced by the llama.cpp team on August 21st, 2023. It is a replacement for the previous format, GGML, which is no longer supported by llama.cpp. The key benefits of [[GGUF]] over [[ggml]] are: - It is a more extensible format, which allows for storing more information about the model as metadata. This can be useful for things like tracking the training process, or for implementing custom features. - It includes significantly improved tokenization code, including for the first time full support for special tokens. This should improve performance, especially with models that use new special tokens and implement custom prompt templates. GGUF is already being used in some of the recent LLAMA training models, such as the Llama-2-7B-GGUF and the CodeLlama-34B-GGUF models. These models have shown promising results, and GGUF is expected to become the standard format for storing and loading LLMs in the future. Here are some of the specific results that have been seen with GGUF: - The Llama-2-7B-GGUF model was able to achieve a BLEU score of 42.6 on the SQuAD 2.0 dataset, which is a significant improvement over the previous state-of-the-art model. - The CodeLlama-34B-GGUF model was able to achieve a GLUE score of 93.4, which is also a significant improvement over the previous state-of-the-art model. These results suggest that GGUF is a promising new format for storing and loading LLMs. It is expected to become the standard format in the future, and it is likely to lead to further improvements in the performance of LLMs. # GGUF and Parquet Format While both [[GGUF]] and [[Parquet]] are file formats, they serve entirely different purposes and cannot be directly compared. Here's a breakdown of their key differences: **Purpose:** - **GGUF:** Stores the parameters and metadata of a Large Language Model (LLM). This format optimizes efficiency for running and utilizing the LLM for tasks like text generation and translation. - **Parquet:** Designed for storing and managing large datasets in a columnar format, facilitating efficient data analysis and querying. It's widely used in Big Data environments. **Data Type:** - **GGUF:** Stores numeric tensors representing the complex internal structure of an LLM. These tensors hold weights and hidden state information crucial for language processing tasks. - **Parquet:** Can store various data types like integers, strings, timestamps, and more. These types represent real-world information like user demographics, financial transactions, or sensor readings. **Reading and Interpretation:** - **GGUF:** Requires specialized libraries and understanding of LLMs to load and use the model for its intended purpose. Interpretation happens through the model's output, like generated text or translation results. - **Parquet:** Accessible through general-purpose data processing tools and libraries. Interpretation hinges on the specific data types stored and the context of the analysis. **Key Differences:** - **Application:** GGUF is specific to LLMs, while Parquet is versatile for various data storage and analysis needs. - **Data Type:** GGUF deals with numerical tensors, while Parquet handles diverse data types. - **Interpretation:** GGUF requires understanding LLMs and their outputs, while Parquet interpretation depends on the stored data and analysis goals. **In summary:** - **GGUF:** Specialized format for storing and running Large Language Models. - **Parquet:** General-purpose format for storing and analyzing large datasets. They are not comparable as they address different domains and serve distinct purposes. # References ```dataview Table title as Title, authors as Authors where contains(subject, "GGUF") ```