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--- |
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license: apache-2.0 |
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inference: false |
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tags: [green, p1, llmware-fx, ov, emerald] |
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--- |
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# slim-extract-tiny-ov |
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**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. |
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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. |
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### Model Description |
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- **Developed by:** llmware |
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- **Model type:** tinyllama |
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- **Parameters:** 1.1 billion |
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- **Model Parent:** llmware/slim-extract-tiny |
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- **Language(s) (NLP):** English |
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- **License:** Apache 2.0 |
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- **Uses:** Extraction of values from complex business documents |
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- **RAG Benchmark Accuracy Score:** NA |
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- **Quantization:** int4 |
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### Example Usage |
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from llmware.models import ModelCatalog |
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text_passage = "The company announced that for the current quarter the total revenue increased by 9% to $125 million." |
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model = ModelCatalog().load_model("slim-extract-tiny-ov") |
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llm_response = model.function_call(text_passage, function="extract", params=["revenue"]) |
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Output: `llm_response = {"revenue": [$125 million"]}` |
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## Model Card Contact |
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[llmware on github](https://www.github.com/llmware-ai/llmware) |
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[llmware on hf](https://www.huggingface.co/llmware) |
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[llmware website](https://www.llmware.ai) |
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