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  tags: [green, llmware-rag, p1, ov]
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  ---
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- # bling-tiny-llama-ov
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- **bling-tiny-llama-ov** is a very small, very fast fact-based question-answering model, designed for retrieval augmented generation (RAG) with complex business documents, and quantized and packaged in OpenVino int4 for AI PCs using Intel GPU, CPU and NPU.
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  This model is one of the smallest and fastest in the series. For higher accuracy, look at larger models in the BLING/DRAGON series.
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  - **RAG Benchmark Accuracy Score:** 86.5
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- Get started right away with [OpenVino](https://github.com/openvinotoolkit/openvino)
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  Looking for AI PC solutions, contact us at [llmware](https://www.llmware.ai)
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  tags: [green, llmware-rag, p1, ov]
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+ # bling-tiny-llama-onnx
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+ **bling-tiny-llama-onnx** is a very small, very fast fact-based question-answering model, designed for retrieval augmented generation (RAG) with complex business documents, and quantized and packaged in ONNX int4 for AI PCs using Intel GPU, CPU and NPU.
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  This model is one of the smallest and fastest in the series. For higher accuracy, look at larger models in the BLING/DRAGON series.
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  - **RAG Benchmark Accuracy Score:** 86.5
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+ Get started right away with [ONNX Runtime](https://github.com/microsoft/onnxruntime)
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  Looking for AI PC solutions, contact us at [llmware](https://www.llmware.ai)
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