Update README.md

README.md

@@ -7,7 +7,7 @@ inference: false
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-bling-phi-3 is part of the BLING ("Best Little Instruct No-GPU") model series, RAG-instruct trained on top of a Microsoft Phi-3 base model.
+bling-phi-3 is part of the BLING ("Best Little Instruct No-GPU") model series, RAG-instruct trained on top of a Microsoft Phi-3.5 base model.
 
 
 ### Benchmark Tests  
@@ -15,10 +15,10 @@ bling-phi-3 is part of the BLING ("Best Little Instruct No-GPU") model series, R
 Evaluated against the benchmark test:   [RAG-Instruct-Benchmark-Tester](https://www.huggingface.co/datasets/llmware/rag_instruct_benchmark_tester)  
 1 Test Run (temperature=0.0, sample=False) 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**:  **99.5** correct out of 100  
---Not Found Classification:  95.0%  
---Boolean:  97.5%  
---Math/Logic:  80.0%  
+--**Accuracy Score**:  **100** correct out of 100  
+--Not Found Classification:  85.0%  
+--Boolean:  95.0%  
+--Math/Logic:  90.0%  
 --Complex Questions (1-5):  4 (Above Average - multiple-choice, causal)  
 --Summarization Quality (1-5):  4 (Above Average)  
 --Hallucinations:  No hallucinations observed in test runs.  
@@ -27,9 +27,6 @@ For test run results (and good indicator of target use cases), please see the fi
 
 Note: compare results with [bling-phi-2](https://www.huggingface.co/llmware/bling-phi-2-v0), and [dragon-mistral-7b](https://www.huggingface.co/llmware/dragon-mistral-7b-v0).    
 
-Note: see also the quantized gguf version of the model- [bling-phi-3-gguf](https://www.huggingface.co/llmware/bling-phi-3-gguf).   
-
-Note: the Pytorch version answered 1 question with "Not Found" while the quantized version answered it correctly, hence the small difference in scores.  
 
 ### Model Description
 
@@ -39,7 +36,7 @@ Note: the Pytorch version answered 1 question with "Not Found" while the quantiz
 - **Model type:** bling
 - **Language(s) (NLP):** English
 - **License:** Apache 2.0
-- **Finetuned from model:** Microsoft Phi-3
+- **Finetuned from model:** Microsoft Phi-3.5
 
 ## Uses
 
@@ -75,8 +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("llmware/bling-phi-3", trust_remote_code=True)  
-    model = AutoModelForCausalLM.from_pretrained("llmware/bling-phi-3", trust_remote_code=True)  
+    tokenizer = AutoTokenizer.from_pretrained("llmware/bling-phi-3.5-gguf", trust_remote_code=True)  
+    model = AutoModelForCausalLM.from_pretrained("llmware/bling-phi-3.5-gguf", trust_remote_code=True)  
 
 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.  
 
@@ -97,29 +94,6 @@ To get the best results, package "my_prompt" as follows:
     my_prompt = {{text_passage}} + "\n" + {{question/instruction}}
 
 
-If you are using a HuggingFace generation script:
-
-    # prepare prompt packaging used in fine-tuning process
-    new_prompt = "<human>: " + entries["context"] + "\n" + entries["query"] + "\n" + "<bot>:"
-
-    inputs = tokenizer(new_prompt, return_tensors="pt")  
-    start_of_output = len(inputs.input_ids[0])
-
-    #   temperature: set at 0.0 with do_sample=False for consistency of output
-    #   max_new_tokens:  set at 100 - may prematurely stop a few of the summaries
-
-    outputs = model.generate(
-            inputs.input_ids.to(device),
-            eos_token_id=tokenizer.eos_token_id,
-            pad_token_id=tokenizer.eos_token_id,
-            do_sample=False,
-            temperature=0.0,
-            max_new_tokens=100,
-            )
-
-    output_only = tokenizer.decode(outputs[0][start_of_output:],skip_special_tokens=True)  
-
-
 ## Model Card Contact
 
 Darren Oberst & llmware team