Update README.md

README.md

@@ -12,6 +12,24 @@ BLING models are fine-tuned with distilled high-quality custom instruct datasets
 the objective of providing a high-quality Instruct model that is 'inference-ready' on a CPU laptop even 
 without using any advanced quantization optimizations.
 
+
+### 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**:  **84.50** correct out of 100  
+--Not Found Classification:  20.0%  
+--Boolean:  66.25%  
+--Math/Logic:  9.4%  
+--Complex Questions (1-5):  1 (Low)  
+--Summarization Quality (1-5):  3 (Coherent, extractive)  
+--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. -->
@@ -94,7 +112,7 @@ This BLING model was built on top of a "Sheared Llama" model base - for more inf
 
 Darren Oberst & llmware team
 
-Please reach out anytime if you are interested in this project and would like to participate and work with us!
+Please reach out anytime if you are interested in this project!