Upload README.md

README.md

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+# SLIM-SA-NER-3B-TOOL
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+**slim-sa-ner-3b-tool** is a 4_K_M quantized GGUF version of slim-sa-ner-3b, providing a small, fast inference implementation, optimized for multi-model concurrent deployment.  
+
+This model combines two of the most popular traditional classifier capabilities (**sentiment analysis** and **named entity recognition**) and re-images them as function calls on a small specialized decoder LLM, generating output in the form of a python dictionary with keys corresponding to sentiment and NER identifiers.  
+
+The intent of SLIMs is to forge a middle-ground between traditional encoder-based classifiers and open-ended API-based LLMs.
+
+The size of the self-contained GGUF model binary is 1.71 GB, which is small enough to run locally on a CPU, and yet which comparables favorably with the use of two traditional FP32 versions of Roberta-Large for NER (1.42GB) and BERT for Sentiment Analysis (440 MB), while offering greater potential capacity depth with 2.7B parameters, and without the requirement of Pytorch and other external dependencies. 
+
+
+[**slim-sa-ner-3b**](https://huggingface.co/llmware/slim-sa-ner-3b) 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-sa-ner-3b-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-sa-ner-3b-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-sa-ner-3b-tool", verbose=True)  
+
+
+Note: please review [**config.json**](https://huggingface.co/llmware/slim-sa-ner-3b-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)