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  This model was converted to GGUF format from [`arcee-ai/Virtuoso-Small`](https://huggingface.co/arcee-ai/Virtuoso-Small) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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  Refer to the [original model card](https://huggingface.co/arcee-ai/Virtuoso-Small) for more details on the model.
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  ## Use with llama.cpp
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  Install llama.cpp through brew (works on Mac and Linux)
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  This model was converted to GGUF format from [`arcee-ai/Virtuoso-Small`](https://huggingface.co/arcee-ai/Virtuoso-Small) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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  Refer to the [original model card](https://huggingface.co/arcee-ai/Virtuoso-Small) for more details on the model.
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+ ---
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+ Model details:
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+ -
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+ Virtuoso-Small
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+ Virtuoso-Small is the debut public release of the Virtuoso series of models by Arcee.ai, designed to bring cutting-edge generative AI capabilities to organizations and developers in a compact, efficient form. With 14 billion parameters, Virtuoso-Small is an accessible entry point for high-quality instruction-following, complex reasoning, and business-oriented generative AI tasks. Its larger siblings, Virtuoso-Medium and Virtuoso-Large, offer even greater capabilities and are available via API at models.arcee.ai.
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+ Key Features
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+ Compact and Efficient: With 14 billion parameters, Virtuoso-Small provides a high-performance solution optimized for smaller hardware configurations without sacrificing quality.
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+ Business-Oriented: Tailored for use cases such as customer support, content creation, and technical assistance, Virtuoso-Small meets the demands of modern enterprises.
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+ Scalable Ecosystem: Part of the Virtuoso series, Virtuoso-Small is fully interoperable with its larger siblings, Forte and Prime, enabling seamless scaling as your needs grow.
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+ Deployment Options
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+ Virtuoso-Small is available under the Apache-2.0 license and can be deployed locally or accessed through an API at models.arcee.ai. For larger-scale or more demanding applications, consider Virtuoso-Forte or Virtuoso-Prime.
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+ ---
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  ## Use with llama.cpp
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  Install llama.cpp through brew (works on Mac and Linux)
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