| --- |
| license: apache-2.0 |
| inference: false |
| base_model: llmware/slim-extract-tiny |
| base_model_relation: quantized |
| 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](https://huggingface.co/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) |
|
|
