Update README.md

README.md

@@ -6,12 +6,32 @@ license: apache-2.0
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-BLING-1.4b-0.1 is the first model release in the BLING ("Best Little Instruction-following No-GPU-required") model series.
+BLING-1.4b-0.1 is part of the BLING ("Best Little Instruction-following No-GPU-required") model series.
 
 BLING models are fine-tuned with distilled high-quality custom instruct datasets, targeted at a specific subset of instruct tasks with 
 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**:  **82.25** correct out of 100  
+--Not Found Classification:  40.0%  
+--Boolean:  61.25%  
+--Math/Logic:  8.75%
+--Complex Questions (1-5):  1 (Low)  
+--Summarization Quality (1-5):  2 (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).
+
+--As a reference point, this model shows substantial improvements in results, compared with the BLING 1.0B Pythia, with fine-tuning and the base training substantially the same.  
+--The model's ability to follow instructions and answer detailed questions improves dramatically from 1.0B -> 1.4B parameters.
+
+
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
@@ -55,6 +75,9 @@ without the need for a lot of complex instruction verbiage - provide a text pass
 
 Any model can provide inaccurate or incomplete information, and should be used in conjunction with appropriate safeguards and fact-checking mechanisms.
 
+Please refer to the benchmark score and testing results for indicator as to the applicability of this model to your intended use case.   
+We have found that this model is reasonably effective and accurate for fact-based, extractive tasks, including key-value, question-answering, and basic summarization.  
+
 
 ## How to Get Started with the Model
 
@@ -97,7 +120,7 @@ BLING models are built on top of EleutherAI/Pythia base - please see citation fo
 
 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!