Upload README.md

README.md

@@ -1,20 +1,14 @@
 ---
 license: apache-2.0
 inference: false 
-tags: [green, p1, llmware-fx,ov]
+tags: [green, p1, llmware-fx, ov, emerald]
 ---
 
-# slim-sentiment-ov
+# slim-extract-tiny-ov  
 
-<!-- Provide a quick summary of what the model is/does. -->
+**slim-extract-tiny-ov** is a specialized function calling model with a single mission to look for values in a text, based on an "extract" key that is passed as a parameter.  No other instructions are required except to pass the context passage, and the target key, and the model will generate a python dictionary consisting of the extract key and a list of the values found in the text, including an 'empty list' if the text does not provide an answer for the value of the selected key.  
 
-**slim-sentiment-ov** is an OpenVino int4 quantized version of slim sentiment 1B, providing a very fast, very small inference implementation, optimized for AI PCs using Intel GPU, CPU and NPU.    
-
-[**slim-sentiment**](https://huggingface.co/llmware/slim-sentiment) is a function-calling specialized model finetuned to evaluate sentiment and return a python dictionary with a sentiment key and the classification value, e.g., "positive", "negative", or "neutral".    
-
-Get started right away with [OpenVino](https://github.com/openvinotoolkit/openvino) 
-
-Looking for AI PC solutions and demos, contact us at [llmware](https://www.llmware.ai)    
+This is an OpenVino int4 quantized version of slim-extract-tiny, providing a very fast, very small inference implementation, optimized for AI PCs using Intel GPU, CPU and NPU.    
 
 
 ### Model Description
@@ -22,16 +16,28 @@ Looking for AI PC solutions and demos, contact us at [llmware](https://www.llmwa
 - **Developed by:** llmware  
 - **Model type:** tinyllama  
 - **Parameters:** 1.1 billion
-- **Model Parent:** llmware/slim-sentiment
+- **Model Parent:** llmware/slim-extract-tiny
 - **Language(s) (NLP):** English  
 - **License:** Apache 2.0  
-- **Uses:** Sentiment classification
+- **Uses:** Extraction of values from complex business documents    
 - **RAG Benchmark Accuracy Score:** NA  
 - **Quantization:** int4  
   
+### Example Usage  
+
+    from llmware.models import ModelCatalog
+
+    text_passage = "The company announced that for the current quarter the total revenue increased by 9% to $125 million."  
+    model = ModelCatalog().load_model("slim-extract-tiny-ov")   
+    llm_response = model.function_call(text_passage, function="extract", params=["revenue"])  
+
+    Output: `llm_response = {"revenue": [$125 million"]}`  
+  
 
 ## Model Card Contact
 
+[llmware on github](https://www.github.com/llmware-ai/llmware)  
+
 [llmware on hf](https://www.huggingface.co/llmware)  
 
 [llmware website](https://www.llmware.ai)