Update README.md

README.md

@@ -6,7 +6,7 @@ license: apache-2.0
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-dragon-mistral-7b-v0 part of the dRAGon ("Delivering RAG On ...") model series, RAG-instruct trained on top of a Mistral-7B base model.
+dragon-deci-7b-v0 part of the dRAGon ("Delivering RAG On ...") model series, RAG-instruct trained on top of a Deci-7B base model.
 
 DRAGON models are fine-tuned with high-quality custom instruct datasets, designed for production quality use in RAG scenarios.
 
@@ -16,10 +16,10 @@ DRAGON models are fine-tuned with high-quality custom instruct datasets, designe
 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**:  **94.75** correct out of 100  
+--**Accuracy Score**:  **97.5** correct out of 100  
 --Not Found Classification:  95.0%  
---Boolean:  94.0%  
---Math/Logic:  77.5%  
+--Boolean:  92.5%  
+--Math/Logic:  91.25%  
 --Complex Questions (1-5):  4 (Low-Medium)  
 --Summarization Quality (1-5):  4 (Coherent, extractive)  
 --Hallucinations:  No hallucinations observed in test runs.  
@@ -31,10 +31,10 @@ For test run results (and good indicator of target use cases), please see the fi
 <!-- Provide a longer summary of what this model is. -->
 
 - **Developed by:** llmware
-- **Model type:** Mistral-7B
+- **Model type:** Deci-7B
 - **Language(s) (NLP):** English
 - **License:** Apache 2.0
-- **Finetuned from model:** Mistral-7B-Base
+- **Finetuned from model:** Deci-7B-Base
 
 ## Uses
 
@@ -72,8 +72,8 @@ 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-mistral-7b-v0")  
-    model = AutoModelForCausalLM.from_pretrained("dragon-mistral-7b-v0")  
+    tokenizer = AutoTokenizer.from_pretrained("dragon-deci-7b-v0")  
+    model = AutoModelForCausalLM.from_pretrained("dragon-deci-7b-v0")  
 
 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.