These files are StarCoder GGML format model files for LoupGarou's WizardCoder Guanaco 15B V1.0.

Please note that these GGMLs are not compatible with llama.cpp, or currently with text-generation-webui. Please see below for a list of tools that work with this GGML model.

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These files are not compatible with llama.cpp or text-generation-webui.

They can be used with:

As other options become available I will endeavour to update them here (do let me know in the Community tab if I've missed something!)

Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.

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The WizardCoder-Guanaco-15B-V1.0 is a language model that combines the strengths of the WizardCoder base model and the openassistant-guanaco dataset for finetuning. The openassistant-guanaco dataset was further trimmed to within 2 standard deviations of token size for input and output pairs and all non-english data has been removed to reduce training size requirements.

This model is built on top of the WizardCoder base model, a large language model known for its impressive capabilities in code related instruction. The WizardCoder base model was further finetuned using QLORA on the openassistant-guanaco dataset to enhance its generative abilities.

However, to ensure more targeted learning and data processing, the dataset was trimmed to within 2 standard deviations of token size for question sets. This process enhances the model's ability to generate more precise and relevant answers, eliminating outliers that could potentially distort the responses. In addition, to focus on English language proficiency, all non-English data has been removed from the Guanaco dataset.

This model is designed to be used for a wide array of text generation tasks that require understanding and generating English text. The model is expected to perform well in tasks such as answering questions, writing essays, summarizing text, translation, and more. However, given the specific data processing and finetuning done, it might be particularly effective for tasks related to English language question-answering systems.

Despite the powerful capabilities of this model, users should be aware of its limitations. The model's knowledge is up to date only until the time it was trained, and it doesn't know about events in the world after that. It can sometimes produce incorrect or nonsensical responses, as it doesn't understand the text in the same way humans do. It should be used as a tool to assist in generating text and not as a sole source of truth.

Here is an example of how to use this model:

The base WizardCoder model was finetuned on the openassistant-guanaco dataset using QLORA, which was trimmed to within 2 standard deviations of token size for question sets and randomized. All non-English data was also removed from this finetuning dataset.

This model, WizardCoder-Guanaco-15B-V1.0, is simply building on the efforts of two great teams to evaluate the performance of a combined model with the strengths of the WizardCoder base model and the openassistant-guanaco dataset.

A sincere appreciation goes out to the developers and the community involved in the creation and refinement of these models. Their commitment to providing open source tools and datasets have been instrumental in making this project a reality.

Moreover, a special note of thanks to the Hugging Face team, whose transformative library has not only streamlined the process of model creation and adaptation, but also democratized the access to state-of-the-art machine learning technologies. Their impact on the development of this project cannot be overstated.