6 Best Orca Type LLM Models

In this article, we delve into a comprehensive exploration of 5 distinct Orca type LLM models. These models, each with their unique characteristics and capabilities, are poised to reshape the landscape of AI language processing. We will provide insightful descriptions of each model, shedding light on their parameter ranges, fine-tuning techniques, and performance benchmarks. By the end of this article, you’ll have a clearer understanding of the advancements and potential these Orca type LLM models bring to the realm of artificial intelligence.

Orca Type LLM Models

OpenOrca-Platypus2-13B

OpenOrca-Platypus2-13B is an innovative merger between garage-bAInd’s Platypus2-13B and Open-Orca’s OpenChat-Preview2-13B, showcasing a remarkable synergy in performance. Developed in collaboration with the Platypus team, this model has set a new benchmark in the landscape of large language models.

Employing the Language Model Evaluation Harness for rigorous benchmarking, the model demonstrates its prowess by achieving a remarkable 112% enhancement over the base Preview2 model in AGI Eval, with an impressive average score of 0.463. A significant part of this success can be attributed to its substantial improvement in LSAT Logical Reasoning performance, attributable to the utilization of STEM and logic-based datasets from garage-bAInd/Platypus2-13B.

Furthermore, Open-Orca/OpenOrcaxOpenChat-Preview2-13B, a key component, contributes its expertise to the blend by leveraging a refined subset of GPT-4 data from the OpenOrca dataset. This strategic integration brings forth a model that not only excels in text generation but also boasts a deep understanding of logical reasoning. For more insights, refer to the model’s associated paper and project webpage, providing comprehensive details on its performance and capabilities.

OpenOrca

OpenOrca is an innovative Orca type LLM model that stands out for its fine-tuning approach using the OpenOrca open dataset. Through careful curation, the model has been trained on a refined selection of 200k GPT-4 entries, skillfully filtered to enhance logical reasoning capabilities by eliminating responses that detract from model reasoning. Remarkably, even with less than 6% of the training data, OpenOrca achieves remarkable results, showcasing state-of-the-art performance within the model class at a minimal training cost of <$200. OpenOrca’s prowess is underscored by its performance on challenging reasoning tasks from BigBench-Hard and AGIEval, with average scores of 0.3753 and 0.3638 respectively. These achievements, relative to the improvements highlighted in the Orca paper, affirm OpenOrca’s remarkable 60% advancement with just 6% of the data—a testament to its efficiency and potential.

Orca Mini v2 (7,13b)

Orca Mini v2 (7, 13b) stands as a significant advancement over its predecessor, Orca Mini v1. This second iteration boasts remarkable improvements across various domains, rectifying the limitations that hindered its predecessor’s usability. Developed through a partnership with Eric Hartford, it leverages the robustness of an Uncensored LLaMA-13b model. The training process involves meticulous fine-tuning on datasets enriched with explanations, benefiting from insights provided by WizardLM, Alpaca, and Dolly-V2 datasets. Additionally, the implementation follows the methodologies outlined in the Orca Research Paper, optimizing dataset construction. The Orca Mini v2 family encompasses two distinct models with parameter counts of 7 billion and 13 billion, offering a versatile toolkit for a wide array of applications.

llama2-13b-orca-8k

llama2-13b-orca-8k is an impressive achievement in the realm of AI language models. It is an expertly fine-tuned version of Meta’s Llama2 13B model, designed specifically for the challenges posed by the long-conversation variant of the Dolphin dataset, known as orca-chat. What sets this model apart is its remarkable ability to comprehend and engage in extensive dialogues, thanks to its context size of 8192 tokens.

While smaller LLMs may struggle with complex logical reasoning, llama2-13b-orca-8k bridges that gap by leveraging insights gained from larger models. The process of transferring step-by-step reasoning from its larger counterparts has led to a leap in its logical reasoning capabilities.

Dolphin-llama

Dolphin-llama is an Orca type LLM model built upon the foundation of llama1, originally developed by Eric Hartford, catering specifically to non-commercial use. While future iterations are set to be trained on llama2 and other openly available models for commercial applications, this model is currently uncensored, aligning with varied requests. The dataset has undergone meticulous filtering to eliminate alignment and bias issues, ensuring compliance.

This model encompasses an extensive dataset, consisting of 842,610 instructions from FLANv2, coupled with GPT-4 completions, alongside 2,625,353 instructions from FLANv2 augmented by GPT-3.5 completions. Adhering to the guidelines outlined in the Orca paper, submix and system prompt distribution have been maintained, with minor exceptions such as the inclusion of the full 75k of CoT in the FLAN-1m dataset. Furthermore, duplicate entries have been meticulously removed to enhance dataset integrity.

StableBeluga

Stable Beluga 2 is a versatile family of models, encompassing a parameter range of 7 billion to 70 billion. This diversity ensures compatibility with a variety of hardware setups. Derived from the renowned Mets’s Llama 2, these models have undergone meticulous fine-tuning on a customized Orca dataset. The results are impressive, showcasing exceptional performance across various tasks.

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