Upload README.md

README.md

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 license: apache-2.0
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+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+**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.
+
+To pull the model via API:  
+
+    from huggingface_hub import snapshot_download           
+    snapshot_download("llmware/slim-sentiment-tool", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False)  
+    
+
+Load in your favorite GGUF inference engine, or try with llmware as follows:
+
+    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)  
+
+    # this one line will download the model and run a series of tests
+    ModelCatalog().tool_test_run("slim-sentiment-tool", verbose=True)  
+
+
+Slim models can also be loaded even more simply as part of a multi-model, multi-step LLMfx calls:
+
+    from llmware.agents import LLMfx
+
+    llm_fx = LLMfx()
+    llm_fx.load_tool("sentiment")
+    response = llm_fx.sentiment(text)  
+
+
+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  
+
+[Any questions? Join us on Discord](https://discord.gg/MhZn5Nc39h)