Update README.md

README.md

@@ -6,13 +6,24 @@ license: apache-2.0
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-**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-tool** 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.  
+slim-sentiment-tool is a 4_K_M quantized GGUF version of slim-sentiment-tool, providing a fast, small inference implementation.
 
-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.  
+Load in your favorite GGUF inference engine, or try with llmware as follows:
 
+    from llmware.models import ModelCatalog
 
+    sentiment_tool = ModelCatalog().load_model("llmware/slim-sentiment-tool")
+    response = sentiment_tool.function_call(text_sample, params=["sentiment"], function="classify")
+
+Slim models can also be loaded even more simply as part of LLMfx calls:
+
+    from llmware.agents import LLMfx
+
+    llm_fx = LLMfx()
+    llm_fx.load_tool("sentiment")
+    response = llm_fx.sentiment(text)
 
 
 ### Model Description
@@ -20,10 +31,10 @@ Each slim model has a corresponding 'tool' in a separate repository, e.g., 'slim
 <!-- Provide a longer summary of what this model is. -->
 
 - **Developed by:** llmware
-- **Model type:** Small, specialized LLM
+- **Model type:** GGUF 
 - **Language(s) (NLP):** English
 - **License:** Apache 2.0
-- **Finetuned from model:** Tiny Llama 1B
+- **Quantized from model:** llmware/slim-sentiment (finetuned tiny llama)
 
 ## Uses
 
@@ -43,36 +54,9 @@ 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("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:
-
-full_prompt = "\<human>\: " + my_prompt + "\n" + "\<bot>\:"
-
-The BLING model was fine-tuned with closed-context samples, which assume generally that the prompt consists of two sub-parts:
-
-1.  Text Passage Context, and
-2.  Specific question or instruction based on the text passage
-
-To get the best results, package "my_prompt" as follows:
-
-my_prompt = {{text_passage}} + "\n" + {{question/instruction}}
-
-
 
 ## Model Card Contact
 
 Darren Oberst & llmware team
 
-Please reach out anytime if you are interested in this project and would like to participate and work with us!
-
-