Update README.md

README.md

@@ -4,11 +4,11 @@ inference: false
 tags: [green, p1, llmware-fx, ov, emerald]
 ---
 
-# slim-extract-tiny-ov 
+# slim-sql-ov 
 
-**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-sql-ov** is a small specialized function calling model that takes as input a table schema and a natural language query, and outputs a SQL statement that corresponds to the query, and can be run against a database table. This is a very small text-to-sql model designed for reasonable accuracy on single tables and relatively straightforwrad queries. 
 
-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. 
+This is an OpenVino int4 quantized version of slim-sql-1b-v0, providing a very fast, very small inference implementation, optimized for AI PCs using Intel GPU, CPU and NPU. 
 
 
 ### Model Description
@@ -16,24 +16,13 @@ This is an OpenVino int4 quantized version of slim-extract-tiny, providing a ver
 - **Developed by:** llmware 
 - **Model type:** tinyllama 
 - **Parameters:** 1.1 billion
-- **Model Parent:** llmware/slim-extract-tiny
+- **Model Parent:** llmware/slim-sql-1b-v0
 - **Language(s) (NLP):** English 
 - **License:** Apache 2.0 
-- **Uses:** Extraction of values from complex business documents 
+- **Uses:** Text-to-SQL conversion 
 - **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)