Update README.md

README.md

@@ -6,7 +6,7 @@ license: apache-2.0
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-dragon-llama-7b-0.1 part of the dRAGon ("Delivering RAG On Private Cloud") model series, RAG-instruct trained on top of a LLama-2 base model.
+dragon-yi-6b-0.1 part of the dRAGon ("Delivering RAG On Private Cloud") model series, RAG-instruct trained on top of a Yi-6B 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**:  **99.0** correct out of 100  
---Not Found Classification:  95.0%  
---Boolean:  82.5%  
---Math/Logic:  70.0%  
+--**Accuracy Score**:  **99.5** correct out of 100  
+--Not Found Classification:  90.0%  
+--Boolean:  87.5%  
+--Math/Logic:  77.5%  
 --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:** LLama-2 
+- **Model type:** Yi
 - **Language(s) (NLP):** English
 - **License:** Apache 2.0
-- **Finetuned from model:** Llama-2-7B-Base
+- **Finetuned from model:** Yi-6B
 
 ## 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 BLING is through direct import in transformers:
 
 from transformers import AutoTokenizer, AutoModelForCausalLM  
-tokenizer = AutoTokenizer.from_pretrained("dragon-llama-7b-0.1")  
-model = AutoModelForCausalLM.from_pretrained("dragon-llama-7b-0.1")  
+tokenizer = AutoTokenizer.from_pretrained("dragon-yi-6b-0.1")  
+model = AutoModelForCausalLM.from_pretrained("dragon-yi-6b-0.1")  
 
 
 The BLING model was fine-tuned with a simple "\<human> and \<bot> wrapper", so to get the best results, wrap inference entries as: