Upload README.md

README.md

@@ -6,47 +6,57 @@ license: apache-2.0
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-bling-red-pajamas-3b-0.1 part of the BLING ("Best Little Instruction-following No-GPU-required") model series, RAG-instruct trained on top of a RedPajama-INCITE-Base-3B-v1 base model.
+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.
 
-BLING models are fine-tuned with distilled high-quality custom instruct datasets, targeted at a specific subset of instruct tasks with 
-the objective of providing a high-quality Instruct model that is 'inference-ready' on a CPU laptop even 
-without using any advanced quantization optimizations.
+DRAGON models are fine-tuned with high-quality custom instruct datasets, designed for production quality use in RAG scenarios.
 
 
+### 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.  
+
+--**Accuracy Score**:  **99.0** correct out of 100  
+--Not Found Classification:  95.0%  
+--Boolean:  82.5%  
+--Math/Logic:  70.0%  
+--Complex Questions (1-5):  4 (Low-Medium)  
+--Summarization Quality (1-5):  4 (Coherent, extractive)  
+--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).
+
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
 
 - **Developed by:** llmware
-- **Model type:** GPTNeoX instruct-trained decoder
+- **Model type:** LLama-2 
 - **Language(s) (NLP):** English
 - **License:** Apache 2.0
-- **Finetuned from model:** togethercomputer/RedPajama-INCITE-Base-3B-v1
+- **Finetuned from model:** Llama-2-7B-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:
+The intended use of DRAGON 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.
 
-1.  Provide high-quality Instruct models that can run on a laptop for local testing.  We have found it extremely useful when building a
-   proof-of-concept, or working with sensitive enterprise data that must be closely guarded, especially in RAG use cases.
+2.  DRAGON models are fine-tuned on top of leading base foundation models, generally in the 6-7B+ range, and purposefully rolled-out across multiple base models to provide choices and "drop-in" replacements for RAG specific use cases.
 
-2.  Push the state of the art for smaller Instruct-following models in the sub-7B parameter range, especially 1B-3B, as single-purpose
-    automation tools for specific tasks through targeted fine-tuning datasets and focused "instruction" tasks.
+3.  DRAGON models were trained on the same principles as the BLING models, so generally, it should be easy to "upgrade" from a BLING model in testing to a DRAGON model in production.
 
 
 ### 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.  Rather than try to be "all things to all people," BLING models try to focus on a narrower set of Instructions more suitable to a ~1-3B parameter GPT model.
-
-BLING is ideal for rapid prototyping, testing, and the ability to perform an end-to-end workflow locally on a laptop without
-having to send sensitive information over an Internet-based API.
+DRAGON is designed for enterprise automation use cases, especially in knowledge-intensive industries, such as financial services,
+legal and regulatory industries with complex information sources.  
 
-The first BLING models have been trained for common RAG scenarios, specifically:   question-answering, key-value extraction, and basic summarization as the core instruction types
+DRAGON 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.
 
 
@@ -62,8 +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("bling-red-pajamas-3b-0.1")  
-model = AutoModelForCausalLM.from_pretrained("bling-red-pajamas-3b-0.1")  
+tokenizer = AutoTokenizer.from_pretrained("dragon-llama-7b-0.1")  
+model = AutoModelForCausalLM.from_pretrained("dragon-llama-7b-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:
@@ -85,7 +95,7 @@ my_prompt = {{text_passage}} + "\n" + {{question/instruction}}
 
 Darren Oberst & llmware team
 
-Please reach out anytime if you are interested in this project and would like to participate and work with us!
+Please reach out anytime if you are interested in this project!