Upload README.md

README.md

@@ -6,57 +6,43 @@ license: apache-2.0
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-**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-tool is a 4_K_M quantized GGUF version of slim-sentiment-tool, providing a fast, small inference implementation.
-
-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)
+**slim-sentiment-tool** is a 4_K_M quantized GGUF version of slim-sentiment, providing a small, fast inference implementation, optimized for multi-model concurrent deployment.  
 
+[**slim-sentiment**](https://huggingface.co/llmware/slim-sentiment) is part of the SLIM ("**S**tructured **L**anguage **I**nstruction **M**odel") series, providing a set of small, specialized decoder-based LLMs, fine-tuned for function-calling.
 
-### Model Description
+To pull the model via API:  
 
-<!-- Provide a longer summary of what this model is. -->
+    from huggingface_hub import snapshot_download           
+    snapshot_download("llmware/slim-sentiment-tool", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False)  
+    
 
-- **Developed by:** llmware
-- **Model type:** GGUF 
-- **Language(s) (NLP):** English
-- **License:** Apache 2.0
-- **Quantized from model:** llmware/slim-sentiment (finetuned tiny llama)
-
-## Uses
+Load in your favorite GGUF inference engine, or try with llmware as follows:
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+    from llmware.models import ModelCatalog  
+    
+    # to load the model and make a basic inference
+    model = ModelCatalog().load_model("slim-sentiment-tool")
+    response = model.function_call(text_sample)  
 
-The intended use of SLIM models is to re-imagine traditional 'hard-coded' classifiers through the use of function calls.  
+    # this one line will download the model and run a series of tests
+    ModelCatalog().tool_test_run("slim-sentiment-tool", verbose=True)  
 
-Example:
 
-    text = "The stock market declined yesterday as investors worried increasingly about the slowing economy."   
+Slim models can also be loaded even more simply as part of a multi-model, multi-step LLMfx calls:
 
-    model generation - {"sentiment": ["negative"]}
+    from llmware.agents import LLMfx
 
-    keys = "sentiment"
+    llm_fx = LLMfx()
+    llm_fx.load_tool("sentiment")
+    response = llm_fx.sentiment(text)  
 
-All of the SLIM models use a novel prompt instruction structured as follows:
 
-    "<human> " + text + "<classify> " + keys + "</classify>" + "/n<bot>: "
+Note: please review [**config.json**](https://huggingface.co/llmware/slim-sentiment-tool/blob/main/config.json) in the repository for prompt wrapping information, details on the model, and full test set.  
 
 
 ## Model Card Contact
 
-Darren Oberst & llmware team
-
+Darren Oberst & llmware team  
 
+[Any questions? Join us on Discord](https://discord.gg/MhZn5Nc39h)