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

# BLING-PHI-2-GGUF

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

**bling-phi-2-gguf** is part of the BLING model series, RAG-instruct trained on top of a Microsoft Phi-2B base model.  

BLING models are fine-tuned with high-quality custom instruct datasets, designed for rapid prototyping in RAG scenarios.  

For other similar models with comparable size and performance in RAG deployments, please see:  

[**bling-phi-3-gguf**](https://huggingface.co/llmware/bling-phi-3-gguf)  
[**bling-stable-lm-3b-4e1t-v0**](https://huggingface.co/llmware/bling-stable-lm-3b-4e1t-v0)  
[**bling-sheared-llama-2.7b-0.1**](https://huggingface.co/llmware/bling-sheared-llama-2.7b-0.1)  
[**bling-red-pajamas-3b-0.1**](https://huggingface.co/llmware/bling-red-pajamas-3b-0.1)  

### Benchmark Tests  

Evaluated against the benchmark test:   [RAG-Instruct-Benchmark-Tester](https://www.huggingface.co/datasets/llmware/rag_instruct_benchmark_tester)  
Average of 2 Test Runs with 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**:  **93.0** correct out of 100  
--Not Found Classification:  95.0%  
--Boolean:  85.0%  
--Math/Logic:  82.5%  
--Complex Questions (1-5):  3 (Above Average - multiple-choice, causal)  
--Summarization Quality (1-5):  3 (Above 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).

### Model Description

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

- **Developed by:** llmware
- **Model type:** Phi-2B
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Finetuned from model:** Microsoft Phi-2B-Base

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

The intended use of BLING models is two-fold:

1.  Provide high-quality RAG-Instruct models designed for fact-based, no "hallucination" question-answering in connection with an enterprise RAG workflow.

2.  BLING models are fine-tuned on top of leading base foundation models, generally in the 1-3B+ range, and purposefully rolled-out across multiple base models to provide choices and "drop-in" replacements for RAG specific use cases.


### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

BLING is designed for enterprise automation use cases, especially in knowledge-intensive industries, such as financial services,
legal and regulatory industries with complex information sources.  

BLING models have been trained for common RAG scenarios, specifically:   question-answering, key-value extraction, and basic summarization as the core instruction types
without the need for a lot of complex instruction verbiage - provide a text passage context, ask questions, and get clear fact-based responses.


## How to Get Started with the Model

To pull the model via API:  

    from huggingface_hub import snapshot_download           
    snapshot_download("llmware/bling-phi-2-gguf", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False)  
    

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

    from llmware.models import ModelCatalog  
    model = ModelCatalog().load_model("bling-phi-2-gguf")            
    response = model.inference(query, add_context=text_sample)  

Note: please review [**config.json**](https://huggingface.co/llmware/bling-phi-2-gguf/blob/main/config.json) in the repository for prompt wrapping information, details on the model, and full test set.  


The BLING model was fine-tuned with a simple "\<human> and \<bot> wrapper", so to get the best results, wrap inference entries as:

    full_prompt = "<human>: " + my_prompt + "\n" + "<bot>:"

The BLING model was fine-tuned with closed-context samples, which assume generally that the prompt consists of two sub-parts:

1.  Text Passage Context, and
2.  Specific question or instruction based on the text passage

To get the best results, package "my_prompt" as follows:

    my_prompt = {{text_passage}} + "\n" + {{question/instruction}}


## Model Card Contact

Darren Oberst & llmware team