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LICENSE.txt

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README.md

@@ -1,3 +1,38 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+inference: false 
+tags: [green, p1, llmware-fx, ov]
+---
+
+# slim-extract-tiny-ov
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+**slim-extract-tiny-ov** 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.    
+
+[**slim-extract-tiny**](https://huggingface.co/llmware/slim-extract-tiny) is a specialized extraction function calling model that looks for a key and value in a complex business document and returns a python dictionary with the designated key and the values found in the text for that key.  
+
+
+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)    
+
+
+### 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  
+  
+
+## Model Card Contact
+
+[llmware on hf](https://www.huggingface.co/llmware)  
+
+[llmware website](https://www.llmware.ai)  

config.json

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+{
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+  "architectures": [
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+  ],
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+  "rope_scaling": null,
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+  "tie_word_embeddings": false,
+  "trained": "custom training",
+  "transformers_version": "4.41.2",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

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+  "transformers_version": "4.41.2"
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+}

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+			<output>
+				<port id="3" precision="STRING" names="string_output">
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="5" name="Result_159885" type="Result" version="opset1">
+			<input>
+				<port id="0" precision="STRING">
+					<dim>-1</dim>
+				</port>
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openvino_model.bin

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openvino_tokenizer.bin

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openvino_tokenizer.xml

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+<?xml version="1.0"?>
+<net name="tokenizer" version="11">
+	<layers>
+		<layer id="0" name="string_input" type="Parameter" version="opset1">
+			<data shape="?" element_type="string" />
+			<output>
+				<port id="0" precision="STRING" names="string_input">
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="1" name="Constant_159868" type="Const" version="opset1">
+			<data element_type="i32" shape="" offset="0" size="4" />
+			<output>
+				<port id="0" precision="I32" />
+			</output>
+		</layer>
+		<layer id="2" name="Constant_159861" type="Const" version="opset1">
+			<data element_type="u8" shape="499723" offset="4" size="499723" />
+			<output>
+				<port id="0" precision="U8">
+					<dim>499723</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="3" name="SentencepieceTokenizer_159864" type="SentencepieceTokenizer" version="extension">
+			<data nbest_size="0" alpha="0" add_bos="true" add_eos="false" reverse="false" />
+			<input>
+				<port id="0" precision="U8">
+					<dim>499723</dim>
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+				<port id="1" precision="STRING">
+					<dim>-1</dim>
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+					<dim>-1</dim>
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+				<port id="3" precision="I32">
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+				<port id="4" precision="I64">
+					<dim>2</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="4" name="Broadcast_159869" type="Broadcast" version="opset3">
+			<data mode="numpy" />
+			<input>
+				<port id="0" precision="I32" />
+				<port id="1" precision="I64">
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+					<dim>-1</dim>
+					<dim>-1</dim>
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+			<output>
+				<port id="0" precision="I32" />
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+		<layer id="6" name="ShapeOf_159871" type="ShapeOf" version="opset3">
+			<data output_type="i64" />
+			<input>
+				<port id="0" precision="I32">
+					<dim>-1</dim>
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+					<dim>1</dim>
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+			<data mode="numpy" />
+			<input>
+				<port id="0" precision="I32" />
+				<port id="1" precision="I64">
+					<dim>1</dim>
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+			<output>
+				<port id="2" precision="I32">
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="8" name="ScatterNDUpdate_159876" type="ScatterNDUpdate" version="opset4">
+			<input>
+				<port id="0" precision="I32">
+					<dim>-1</dim>
+					<dim>-1</dim>
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+				<port id="1" precision="I64">
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+				<port id="3" precision="I32">
+					<dim>-1</dim>
+					<dim>-1</dim>
+				</port>
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+		</layer>
+		<layer id="9" name="ScatterNDUpdate_159876" type="Convert" version="opset1">
+			<data destination_type="i64" />
+			<input>
+				<port id="0" precision="I32">
+					<dim>-1</dim>
+					<dim>-1</dim>
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+			<output>
+				<port id="1" precision="I64" names="attention_mask">
+					<dim>-1</dim>
+					<dim>-1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="11" name="Constant_159865" type="Const" version="opset1">
+			<data element_type="i32" shape="" offset="0" size="4" />
+			<output>
+				<port id="0" precision="I32" />
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+		<layer id="12" name="Broadcast_159866" type="Broadcast" version="opset3">
+			<data mode="numpy" />
+			<input>
+				<port id="0" precision="I32" />
+				<port id="1" precision="I64">
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+			<output>
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+					<dim>-1</dim>
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+		</layer>
+		<layer id="13" name="ScatterNDUpdate_159867" type="ScatterNDUpdate" version="opset4">
+			<input>
+				<port id="0" precision="I32">
+					<dim>-1</dim>
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+		<layer id="14" name="ScatterNDUpdate_159867" type="Convert" version="opset1">
+			<data destination_type="i64" />
+			<input>
+				<port id="0" precision="I32">
+					<dim>-1</dim>
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+		<layer id="15" name="Result_159877" type="Result" version="opset1">
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special_tokens_map.json

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+{
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.model

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+size 499723

tokenizer_config.json

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+{
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+  "added_tokens_decoder": {
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+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}