Chupacabra 7B (GGUF files for llama.cpp/lmstudio/kobold.cpp/gpt4all/textgen-web-ui)

Model Description

This model was made by merging models based on Mistral with the SLERP merge method.

Advantages of SLERP vs averaging weights(common) are as follows:

Spherical Linear Interpolation (SLERP) - Traditionally, model merging often resorts to weight averaging which, although straightforward, might not always capture the intricate features of the models being merged. The SLERP technique addresses this limitation, producing a blended model with characteristics smoothly interpolated from both parent models, ensuring the resultant model captures the essence of both its parents.
Smooth Transitions - SLERP ensures smoother transitions between model parameters. This is especially significant when interpolating between high-dimensional vectors.
Better Preservation of Characteristics - Unlike weight averaging, which might dilute distinct features, SLERP preserves the curvature and characteristics of both models in high-dimensional spaces.
Nuanced Blending - SLERP takes into account the geometric and rotational properties of the models in the vector space, resulting in a blend that is more reflective of both parent models' characteristics.

List of all models and merging path is coming soon.

Purpose

Merging the "thick"est model weights from mistral models using amazing training methods like deep probabilistic optimization (dpo) and reinforced learning.

I have spent countless hours studying the latest research papers, attending conferences, and networking with experts in the field. I experimented with different algorithms, tactics, fine-tuned hyperparameters, optimizers, and optimized code until i achieved the best possible results.

Thank you openchat 3.5 for showing me the way.

Here is my contribution.

Prompt Template

Replace {system} with your system prompt, and {prompt} with your prompt instruction.

GPT4 System: {system}<|end_of_turn|>GPT4 User: {prompt}<|end_of_turn|>GPT4 Assistant:

Bug fixes

Fixed issue with generation and the incorrect model weights. Model weights have been corrected and now generation works again. Reuploading GGUF to the GGUF repository as well as the AWQ versions.
Developed by: Ray Hernandez
Model type: Mistral
Language(s) (NLP): English
License: Apache 2.0

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Uses

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

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Summary

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