Update README.md

README.md

@@ -6,11 +6,13 @@ 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.
+**slim-sentiment** is part of the SLIM ("Structured Language Instruction Model") model series, providing a set of small, specialized decoder-based LLMs, fine-tuned for function-calling.  
+
+slim-sentiment has been fine-tuned for **sentiment analysis** function calls, with output of JSON dictionary corresponding to specific named entity keys.  
+
+Each slim model has a corresponding 'tool' in a separate repository, e.g., 'slim-sentiment-tool', which a 4-bit quantized gguf version of the model that is intended to be used for inference.  
+
 
-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.
 
 
 ### Model Description
@@ -18,52 +20,37 @@ without using any advanced quantization optimizations.
 <!-- Provide a longer summary of what this model is. -->
 
 - **Developed by:** llmware
-- **Model type:** GPTNeoX instruct-trained decoder
+- **Model type:** Small, specialized LLM
 - **Language(s) (NLP):** English
 - **License:** Apache 2.0
-- **Finetuned from model:** togethercomputer/RedPajama-INCITE-Base-3B-v1
+- **Finetuned from model:** Tiny Llama 1B
 
 ## 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 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.  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.
-
-
-### 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.
+The intended use of SLIM models is to re-imagine traditional 'hard-coded' classifiers through the use of function calls.  
 
-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
-without the need for a lot of complex instruction verbiage - provide a text passage context, ask questions, and get clear fact-based responses.
+Example:
 
+    text = "The stock market declined yesterday as investors worried increasingly about the slowing economy."   
 
-## Bias, Risks, and Limitations
+    model generation - {"sentiment": ["negative"]}
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+    keys = "sentiment"
 
-Any model can provide inaccurate or incomplete information, and should be used in conjunction with appropriate safeguards and fact-checking mechanisms.
+All of the SLIM models use a novel prompt instruction structured as follows:
 
+    "<human> " + text + "<classify> " + keys + "</classify>" + "/n<bot>: "
 
+=
 ## How to Get Started with the Model
 
 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("slim-sentiment")  
+model = AutoModelForCausalLM.from_pretrained("slim-sentiment")  
 
 
 The BLING model was fine-tuned with a simple "\<human> and \<bot> wrapper", so to get the best results, wrap inference entries as: