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README.md
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---
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license: mit
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---
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## Overview
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Deepscaler is an advanced AI model developed from the agentica-org's DeepScaleR-1.5B-Preview, designed to enhance the efficiency and scalability of various machine learning tasks. Its core purpose is to provide high-quality predictive analytics and data processing capabilities while optimizing resource usage. Deepscaler is particularly useful in scenarios such as natural language processing, computer vision, and more complex data interpretation tasks, making it suitable for applications in industries like finance, healthcare, and entertainment. Users can leverage its performance to achieve faster training times and improved accuracy in their models. Overall, Deepscaler's architecture allows it to deliver robust results with reduced computational overhead, making it an excellent choice for developers and organizations aiming to scale their AI solutions.
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## Variants
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| No | Variant | Cortex CLI command |
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| --- | --- | --- |
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| 1 | [gguf](https://huggingface.co/cortexso/deepscaler
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## Use it with Jan (UI)
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1. Install **Jan** using [Quickstart](https://jan.ai/docs/quickstart)
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2. Use in Jan model Hub:
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cortexso/deepscaler
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## Use it with Cortex (CLI)
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1. Install **Cortex** using [Quickstart](https://cortex.jan.ai/docs/quickstart)
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2. Run the model with command:
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cortex run deepscaler
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## Credits
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- **Author:** agentica-org
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- **Converter:** [Homebrew](https://www.homebrew.ltd/)
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- **Original License:** [LICENSE](https://huggingface.co/agentica-org/DeepScaleR-1.5B-Preview/blob/main/LICENSE)
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---
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license: mit
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pipeline_tag: text-generation
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tags:
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- cortex.cpp
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---
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## Overview
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Deepscaler is an advanced AI model developed from the agentica-org's DeepScaleR-1.5B-Preview, designed to enhance the efficiency and scalability of various machine learning tasks. Its core purpose is to provide high-quality predictive analytics and data processing capabilities while optimizing resource usage. Deepscaler is particularly useful in scenarios such as natural language processing, computer vision, and more complex data interpretation tasks, making it suitable for applications in industries like finance, healthcare, and entertainment. Users can leverage its performance to achieve faster training times and improved accuracy in their models. Overall, Deepscaler's architecture allows it to deliver robust results with reduced computational overhead, making it an excellent choice for developers and organizations aiming to scale their AI solutions.
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## Variants
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| No | Variant | Cortex CLI command |
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| --- | --- | --- |
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| 1 | [gguf](https://huggingface.co/cortexso/deepscaler/tree/main) | cortex run deepscaler |
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## Use it with Jan (UI)
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1. Install **Jan** using [Quickstart](https://jan.ai/docs/quickstart)
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2. Use in Jan model Hub:
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```bash
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cortexso/deepscaler
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```
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## Use it with Cortex (CLI)
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1. Install **Cortex** using [Quickstart](https://cortex.jan.ai/docs/quickstart)
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2. Run the model with command:
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```bash
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cortex run deepscaler
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```
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## Credits
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- **Author:** agentica-org
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- **Converter:** [Homebrew](https://www.homebrew.ltd/)
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- **Original License:** [LICENSE](https://huggingface.co/agentica-org/DeepScaleR-1.5B-Preview/blob/main/LICENSE)
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