README.md

---
license: apache-2.0  
inference: false  
---

BLING-QWEN-NANO-TOOL


**bling-qwen-nano-tool** is a RAG-finetuned version on Qwen2-0.5B for use in fact-based context question-answering, packaged with 4_K_M GGUF quantization, providing a very fast, very small inference implementation for use on CPUs.  

To pull the model via API:  

    from huggingface_hub import snapshot_download           
    snapshot_download("llmware/bling-qwen-nano-tool", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False)  


## Benchmark Tests  

Evaluated against the benchmark test: RAG-Instruct-Benchmark-Tester
1 Test Run with sample=False & temperature=0.0 (deterministic output) - 1 point for correct answer, 0.5 point for partial correct or blank / NF, 0.0 points for incorrect, and -1 points for hallucinations.

--Accuracy Score: **81.0** correct out of 100  
--Not Found Classification: 65.0%  
--Boolean: 62.5%  
--Math/Logic: 42.5%  
--Complex Questions (1-5): 3 (Average for ~1B model)  
--Summarization Quality (1-5): 3 (Average)  
--Hallucinations: No hallucinations observed in test runs.  

For test run results (and good indicator of target use cases), please see the files ("core_rag_test" and "answer_sheet" in this repo).


Load in your favorite GGUF inference engine, or try with llmware as follows:

    from llmware.models import ModelCatalog  
    model = ModelCatalog().load_model("bling-qwen-nano-tool")            
    response = model.inference(query, add_context=text_sample)  

Note: please review [**config.json**](https://huggingface.co/llmware/bling-qwen-nano-tool/blob/main/config.json) in the repository for prompt wrapping information, details on the model, and full test set.  


### Model Description

<!-- Provide a longer summary of what this model is. -->

- **Developed by:** llmware
- **Model type:** GGUF 
- **Language(s) (NLP):** English
- **License:** Apache 2.0  

## Model Card Contact

Darren Oberst & llmware team