Update README.md

README.md

@@ -1,34 +1,26 @@
 ---
-license: apache-2.0  
-inference: false  
+license: llama3.1
+inference: false
 ---
 
-# Model Card for Model ID
+# DRAGON-LLAMA-3.1-GGUF
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-bling-phi-2-v0 is part of the BLING ("Best Little Instruct No GPU Required ...") model series, RAG-instruct trained on top of a Microsoft Phi-2B base model.
-
-BLING models are fine-tuned with high-quality custom instruct datasets, designed for rapid prototyping in RAG scenarios.
-
-For models with comparable size and performance in RAG deployments, please see:  
-
-[**bling-stable-lm-3b-4e1t-v0**](https://huggingface.co/llmware/bling-stable-lm-3b-4e1t-v0)  
-[**bling-sheared-llama-2.7b-0.1**](https://huggingface.co/llmware/bling-sheared-llama-2.7b-0.1)  
-[**bling-red-pajamas-3b-0.1**](https://huggingface.co/llmware/bling-red-pajamas-3b-0.1)  
+dragon-llama-3.1-gguf is RAG-instruct trained on top of a Llama-3.1 base model.
 
 ### Benchmark Tests  
 
 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.  
+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**:  **93.0** correct out of 100  
---Not Found Classification:  95.0%  
---Boolean:  85.0%  
---Math/Logic:  82.5%  
---Complex Questions (1-5):  3 (Above Average - multiple-choice, causal)  
---Summarization Quality (1-5):  3 (Above Average)  
---Hallucinations:  No hallucinations observed in test runs.  
+--**Accuracy Score**:  **94.0** correct out of 100  
+--Not Found Classification:  70.0%  
+--Boolean:  90.0%  
+--Math/Logic:  72.5%  
+--Complex Questions (1-5):  4 (Above Average - table-reading, causal)  
+--Summarization Quality (1-5):  4 (Above Average)  
+--Hallucinations:  No hallucinations but a few instances of drawing on 'background' knowledge.  
 
 For test run results (and good indicator of target use cases), please see the files ("core_rag_test" and "answer_sheet" in this repo).
 
@@ -39,29 +31,9 @@ For test run results (and good indicator of target use cases), please see the fi
 - **Developed by:** llmware
 - **Model type:** Phi-2B
 - **Language(s) (NLP):** English
-- **License:** Apache 2.0
-- **Finetuned from model:** Microsoft Phi-2B-Base
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-The intended use of BLING models is two-fold:
-
-1.  Provide high-quality RAG-Instruct models designed for fact-based, no "hallucination" question-answering in connection with an enterprise RAG workflow.
-
-2.  BLING models are fine-tuned on top of leading base foundation models, generally in the 1-3B+ range, and purposefully rolled-out across multiple base models to provide choices and "drop-in" replacements for RAG specific use cases.
-
+- **License:** Llama-3.1 Community License
+- **Finetuned from model:** Llama-3.1-Base
 
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-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.  
-
-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.
 
 
 ## Bias, Risks, and Limitations
@@ -76,8 +48,8 @@ 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("bling-phi-2-v0", trust_remote_code=True)  
-    model = AutoModelForCausalLM.from_pretrained("bling-phi-2-v0", trust_remote_code=True)  
+    tokenizer = AutoTokenizer.from_pretrained("dragon-llama-3.1-gguf", trust_remote_code=True)  
+    model = AutoModelForCausalLM.from_pretrained("dragon-llama-3.1-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.  
 
@@ -95,27 +67,6 @@ 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 0.3 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=True,
-            temperature=0.3,
-            max_new_tokens=100,
-            )
-
-    output_only = tokenizer.decode(outputs[0][start_of_output:],skip_special_tokens=True)  
 
 
 ## Model Card Contact