Upload README.md

README.md

@@ -1,27 +1,30 @@
 ---
-license: cc-by-sa-4.0
+license: apache-2.0
 ---
 
-# SLIM-XSUM-TOOL
+# SLIM-QA-GEN-TINY-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.  
+**slim-qa-gen-tiny-tool** is a 4_K_M quantized GGUF version of slim-qa-gen-tiny, 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:  
+This model implements a generative 'question' and 'answer' (e.g., 'qa-gen') function, which takes a context passage as an input, and then generates as an output a python dictionary consisting of two keys:  
 
-     `{'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.  
+     `{'question': ['What was the amount of revenue in the quarter?'], 'answer': ['$3.2 billion']} `  
 
-[**slim-xsum**](https://huggingface.co/llmware/slim-xsum) is the Pytorch version of the model, and suitable for fine-tuning for further domain adaptation.
+The model has been designed to accept one of three different parameters to guide the type of question-answer created:  'question, answer' (generates a standard question and answer), 'boolean' (generates a 'yes-no' question and answer), and 'multiple choice' (generates a multiple choice question and answer).  
+
+slim-qa-gen-tiny-tool is a fine-tune of a tinyllama (1b) parameter model, designed for fast, local deployment and rapid testing and prototyping.   Please also see slim-qa-gen-phi-3-tool, which is finetune of phi-3, and will provide higher-quality results, at the trade-off of slightly slower performance and requiring more memory.  
+
+
+[**slim-qa-gen-tiny*](https://huggingface.co/llmware/slim-qa-gen-tiny) 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)  
+    snapshot_download("llmware/slim-qa-gen-tiny-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:
@@ -29,11 +32,11 @@ 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")
+    model = ModelCatalog().load_model("slim-qa-gen-tiny-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)  
+    ModelCatalog().tool_test_run("slim-qa-gen-tiny-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.