Update README.md

README.md

@@ -18,16 +18,16 @@ Evaluated against the benchmark test:   [RAG-Instruct-Benchmark-Tester](https://
 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**:  **98.0** correct out of 100  
---Not Found Classification:  85.0%  
---Boolean:  100.0%  
---Math/Logic:  92.5%  
+--Not Found Classification:  90.0%  
+--Boolean:  97.5%  
+--Math/Logic:  95%  
 --Complex Questions (1-5):  5 (Best in Class)  
---Summarization Quality (1-5):  3 (Average)  
+--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).
 
-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).  
+Please note that these test results were achieved using the 4_K_M quantized version of this model - [dragon-yi-9b-gguf](https://www.huggingface.co/llmware/dragon-yi-9b-gguf).  
 
 Note: compare results with [dragon-mistral-0.3-gguf](https://www.huggingface.co/llmware/dragon-mistral-0.3-gguf).  
 
@@ -37,10 +37,10 @@ Note: compare results with [dragon-mistral-0.3-gguf](https://www.huggingface.co/
 <!-- Provide a longer summary of what this model is. -->
 
 - **Developed by:** llmware
-- **Model type:** Qwen
+- **Model type:** Yi-9B
 - **Language(s) (NLP):** English
 - **License:** Apache 2.0
-- **Finetuned from model:** Qwen2-7b-base
+- **Finetuned from model:** Yi-9b-base
 
 
 ### Direct Use
@@ -66,8 +66,8 @@ 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("dragon-yi-1.5v-9b")  
+    model = AutoModelForCausalLM.from_pretrained("dragon-yi-1.5v-9b")  
 
 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.