File size: 1,286 Bytes
0e98d7e 09c454b 0e98d7e 09c454b 0e98d7e 09c454b 0e98d7e 09c454b 0e98d7e 09c454b 2d0fb3b 09c454b 0e98d7e 2d0fb3b 09c454b 0e98d7e 2d0fb3b 0e98d7e 2d0fb3b 0e98d7e 2d0fb3b 0e98d7e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 |
---
license: apache-2.0
inference: false
tags: [green, llmware-rag, p3, ov]
---
# bling-phi-3-ov
**bling-phi-3-ov** is a fast and accurate 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.
This model is one of the most accurate in the BLING/DRAGON model series, which is especially notable given the relative small size and is ideal for use on AI PCs and local inferencing.
### Model Description
- **Developed by:** llmware
- **Model type:** phi-3
- **Parameters:** 3.8 billion
- **Quantization:** int4
- **Model Parent:** [llmware/bling-phi-3](https://www.huggingface.co/llmware/bling-phi-3)
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Uses:** Fact-based question-answering, RAG
- **RAG Benchmark Accuracy Score:** 99.5
Get started right away with [OpenVino](https://github.com/openvinotoolkit/openvino)
Looking for AI PC solutions, contact us at [llmware](https://www.llmware.ai)
## Model Card Contact
[llmware on github](https://www.github.com/llmware-ai/llmware)
[llmware on hf](https://www.huggingface.co/llmware)
[llmware website](https://www.llmware.ai)
|