Update README.md

README.md

@@ -3,13 +3,13 @@ license: apache-2.0
 inference: false 
 ---
 
-# DRAGON-QWEN-7B
+# BLING-QWEN-1.5B
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-dragon-qwen-7b is part of the dRAGon ("Delivering RAG On ...") model series, RAG-instruct trained on top of a Qwen2 7b base model.
+bling-qwen-1.5b is part of the BLING model series, RAG-instruct trained on top of a Qwen2 1.5b base model.
 
-DRAGON models have been fine-tuned with the specific objective of fact-based question-answering over complex business and legal documents with an emphasis on reducing hallucinations and providing short, clear answers for workflow automation.
+BLING models have been fine-tuned with the specific objective of fact-based question-answering over complex business and legal documents with an emphasis on reducing hallucinations and providing short, clear answers for workflow automation.
 
 
 ### Benchmark Tests  
@@ -17,19 +17,17 @@ DRAGON models have been fine-tuned with the specific objective of fact-based que
 Evaluated against the benchmark test:   [RAG-Instruct-Benchmark-Tester](https://www.huggingface.co/datasets/llmware/rag_instruct_benchmark_tester)  
 1 Test Run with sample=False & temperature=0.0 (deterministic output) - 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**:  **99.0** correct out of 100  
---Not Found Classification:  85.0%  
---Boolean:  100.0%  
---Math/Logic:  92.5%  
---Complex Questions (1-5):  5 (Best in Class)  
+--**Accuracy Score**:  **93.5** correct out of 100  
+--Not Found Classification:  75.0%  
+--Boolean:  87.5%  
+--Math/Logic:  70%  
+--Complex Questions (1-5):  3 (Best in Class)  
 --Summarization Quality (1-5):  3 (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).
 
-Please note that these test results were achieved using the 4_K_M quantized version of this model - [dragon-qwen-7b-gguf](https://www.huggingface.co/llmware/dragon-qwen-7b-gguf).  
-
-Note: compare results with [dragon-mistral-0.3-gguf](https://www.huggingface.co/llmware/dragon-mistral-0.3-gguf).  
+Please note that these test results were achieved using the 4_K_M quantized version of this model - [bling-qwen-mini-tool](https://www.huggingface.co/llmware/bling-qwen-mini-tool).  
 
 
 ### Model Description
@@ -40,17 +38,17 @@ Note: compare results with [dragon-mistral-0.3-gguf](https://www.huggingface.co/
 - **Model type:** Qwen
 - **Language(s) (NLP):** English
 - **License:** Apache 2.0
-- **Finetuned from model:** Qwen2-7b-base
+- **Finetuned from model:** Qwen2-1.5b-base
 
 
 ### Direct Use
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-DRAGON is designed for enterprise automation use cases, especially in knowledge-intensive industries, such as financial services,
+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.  
 
-DRAGON models have been trained for common RAG scenarios, specifically:   question-answering, key-value extraction, and basic summarization as the core instruction types
+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.
 
 
@@ -66,8 +64,8 @@ Any model can provide inaccurate or incomplete information, and should be used i
 The fastest way to get started with dRAGon is through direct import in transformers:
 
     from transformers import AutoTokenizer, AutoModelForCausalLM  
-    tokenizer = AutoTokenizer.from_pretrained("dragon-qwen-7b")  
-    model = AutoModelForCausalLM.from_pretrained("dragon-qwen-7b")  
+    tokenizer = AutoTokenizer.from_pretrained("llmware/bling-qwen-1.5b")  
+    model = AutoModelForCausalLM.from_pretrained("llmware/bling-qwen-1.5b")  
 
 Please refer to the generation_test .py files in the Files repository, which includes 200 samples and script to test the model.  The **generation_test_llmware_script.py** includes built-in llmware capabilities for fact-checking, as well as easy integration with document parsing and actual retrieval to swap out the test set for RAG workflow consisting of business documents.