Upload README.md

README.md

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-license: apache-2.0
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+---
+license: cc-by-sa-4.0
+---
+
+# SLIM-XSUM-TOOL
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+**slim-xsum-tool** is a 4_K_M quantized GGUF version of slim-xsum, providing a small, fast inference implementation, optimized for multi-model concurrent deployment.  
+
+This model implements an 'extreme summarization' (e.g., 'xsum') function based on the parameter key "xsum" that generates an LLM text output in the form of a python dictionary as follows:  
+
+     `{'xsum': ['Stock Market declines on worries of interest rates.']} `  
+     
+The intent of SLIMs is to forge a middle-ground between traditional encoder-based classifiers and open-ended API-based LLMs through the use of function-calling and small specialized LLMs.  
+
+[**slim-xsum**](https://huggingface.co/llmware/slim-xsum) is the Pytorch version of the model, and suitable for fine-tuning for further domain adaptation.
+
+
+To pull the model via API:  
+
+    from huggingface_hub import snapshot_download           
+    snapshot_download("llmware/slim-xsum-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-xsum-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-xsum-tool", verbose=True)  
+
+
+Note: please review [**config.json**](https://huggingface.co/llmware/slim-xsum-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)