Update README.md

README.md

@@ -3,29 +3,13 @@ license: apache-2.0
 inference: false  
 ---
 
-# dragon-phi-3-answer-tool
+# slim-q-gen-phi-3
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-dragon-phi-3-answer-tool is part of the DRAGON ("Delivering RAG On ...") model series, RAG-instruct trained on top of a Microsoft Phi-3 base model.
+slim-q-gen-phi-3 is a specialized function-calling model, finetuned on top of a phi-3-mini-4k base, to generate a python dictionary consisting of a single key "question".
 
-DRAGON models are fine-tuned with high-quality custom instruct datasets, designed for production use in RAG scenarios.
-
-
-### 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**:  **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).
+For most inference use cases, we would recommend using the quantized 'tool' version of this model - [slim-q-gen-phi-3-tool](https://huggingface.co/llmware/slim-q-gen-phi-3-tool)  
 
 ### Model Description
 
@@ -37,27 +21,6 @@ For test run results (and good indicator of target use cases), please see the fi
 - **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