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--- |
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license: apache-2.0 |
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inference: false |
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base_model: llmware/bling-tiny-llama-v0 |
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base_model_relation: quantized |
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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, 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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### Model Description |
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- **Developed by:** llmware |
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- **Model type:** tinyllama |
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- **Parameters:** 1.1 billion |
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- **Quantization:** int4 |
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- **Model Parent:** [llmware/bling-tiny-llama-v0](https://www.huggingface.co/llmware/bling-tiny-llama-v0) |
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- **Language(s) (NLP):** English |
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- **License:** Apache 2.0 |
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- **Uses:** Fact-based question-answering, RAG |
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- **RAG Benchmark Accuracy Score:** 86.5 |
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## Model Card Contact |
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[llmware on github](https://www.github.com/llmware-ai/llmware) |
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[llmware on hf](https://www.huggingface.co/llmware) |
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[llmware website](https://www.llmware.ai) |
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