---
license: apache-2.0
inference: false 
tags: [green, llmware-rag, p1, ov]
---

# bling-tiny-llama-ov

<!-- Provide a quick summary of what the model is/does. -->

**bling-tiny-llama-ov** is an OpenVino int4 quantized version of BLING Tiny-Llama 1B, providing a very fast, very small inference implementation, optimized for AI PCs using Intel GPU, CPU and NPU.    

[**bling-tiny-llama**](https://huggingface.co/llmware/bling-tiny-llama-v0) is a fact-based question-answering model, optimized for complex business documents.  

Get started right away

1.  Install dependencies

  ```
  pip3 install llmware
  pip3 install openvino
  pip3 install openvino_genai
  ```

2.  Hello World

  ```
  from llmware.models import ModelCatalog
  model = ModelCatalog().load_model("bling-tiny-llama-ov")
  response = model.inference("The stock price is $45.\nWhat is the stock price?")
  print("response: ", response)
  ```
    
Get started right away with [OpenVino](https://github.com/openvinotoolkit/openvino) 

Looking for AI PC solutions and demos, contact us at [llmware](https://www.llmware.ai)    


### Model Description

- **Developed by:** llmware  
- **Model type:** tinyllama  
- **Parameters:** 1.1 billion
- **Model Parent:** llmware/bling-tiny-llama-v0  
- **Language(s) (NLP):** English  
- **License:** Apache 2.0  
- **Uses:** Fact-based question-answering  
- **RAG Benchmark Accuracy Score:** 86.5  
- **Quantization:** int4  
  

## Model Card Contact

[llmware on hf](https://www.huggingface.co/llmware)  

[llmware website](https://www.llmware.ai)