Upload README.md

README.md

@@ -1,3 +1,91 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0  
+inference: false  
+---
+
+# bling-phi-3-gguf
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+bling-phi-3-gguf is part of the BLING ("Best Little Instruct No-GPU") model series, RAG-instruct trained for fact-based question-answering use cases on top of a Microsoft Phi-3 base model.
+
+
+### Benchmark Tests  
+
+Evaluated against the benchmark test:   [RAG-Instruct-Benchmark-Tester](https://www.huggingface.co/datasets/llmware/rag_instruct_benchmark_tester)  
+1 Test Run (with temperature = 0.0 and sample = False) 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**:  **100.0** correct out of 100  
+--Not Found Classification:  95.0%  
+--Boolean:  97.5%  
+--Math/Logic:  80.0%  
+--Complex Questions (1-5):  4 (Above Average - multiple-choice, causal)  
+--Summarization Quality (1-5):  4 (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).  
+
+Note: compare results with [bling-phi-2](https://www.huggingface.co/llmware/bling-phi-2-v0), and [dragon-mistral-7b](https://www.huggingface.co/llmware/dragon-mistral-7b-v0).  
+
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+- **Developed by:** llmware
+- **Model type:** bling-rag-instruct 
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model:** Microsoft Phi-3
+
+## 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.
+
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+BLING models are designed to operate with grounded sources, e.g., inclusion of a context passage in the prompt, and will not yield consistent or positive results if open-context prompting in which you are looking for the model to draw upon potential background knowledge of the world - in fact, it is likely that the BLING will respond with a simple "Not Found." to an open context query.  
+
+Any model can provide inaccurate or incomplete information, and should be used in conjunction with appropriate safeguards and fact-checking mechanisms.
+
+
+## How to Get Started with the Model
+
+To pull the model via API:  
+
+    from huggingface_hub import snapshot_download           
+    snapshot_download("llmware/bling-phi-3-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  
+    
+    # to load the model and make a basic inference
+    model = ModelCatalog().load_model("llmware/bling-phi-3-gguf", temperature=0.0, sample=False)
+    response = model.inference(query, add_context=text_sample)  
+
+Details on the prompt wrapper and other configurations are on the config.json file in the files repository.  
+
+## Model Card Contact
+
+Darren Oberst & llmware team