Upload README.md

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+inference: false 
+tags: [green, p1, llmware-fx, ov, emerald]
+---
+
+# slim-extract-tiny-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. 
+
+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
+
+- **Developed by:** llmware 
+- **Model type:** tinyllama 
+- **Parameters:** 1.1 billion
+- **Model Parent:** llmware/slim-extract-tiny
+- **Language(s) (NLP):** English 
+- **License:** Apache 2.0 
+- **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)