Upload README.md

README.md

@@ -1,3 +1,58 @@
 ---
 license: apache-2.0
 ---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+**slim-ner-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-ner-tool is a 4_K_M quantized GGUF version of slim-ner, providing a small, fast inference implementation.
+
+Load in your favorite GGUF inference engine (see details in config.json to set up the prompt template), or try with llmware as follows:
+
+    from llmware.models import ModelCatalog
+
+    # to load the model and make a basic inference
+    ner_tool = ModelCatalog().load_model("slim-ner-tool")
+    response = ner_tool.function_call(text_sample)  
+
+    # this one line will download the model and run a series of tests
+    ModelCatalog().test_run("slim-ner-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("ner")
+    response = llm_fx.named_entity_extraction(text)
+
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+- **Developed by:** llmware
+- **Model type:** GGUF 
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Quantized from model:** llmware/slim-sentiment (finetuned tiny llama)
+
+## 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. -->
+
+SLIM models provide a fast, flexible, intuitive way to integrate classifiers and structured function calls into RAG and LLM application workflows.  
+
+Model instructions, details and test samples have been packaged into the config.json file in the repository, along with the GGUF file.   
+
+
+
+## Model Card Contact
+
+Darren Oberst & llmware team
+
+