openvino-ci
commited on
Commit
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Upload folder using huggingface_hub
Browse files- README.md +18 -41
- config.json +5 -2
- generation_config.json +1 -1
- openvino_detokenizer.bin +2 -2
- openvino_detokenizer.xml +24 -29
- openvino_model.bin +2 -2
- openvino_model.xml +0 -0
- openvino_tokenizer.bin +2 -2
- openvino_tokenizer.xml +190 -370
- tokenizer.json +0 -0
README.md
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license_link: https://choosealicense.com/licenses/apache-2.0/
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---
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# RedPajama-INCITE-Chat-3B-v1-int8-ov
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* Model creator: [
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* Original model: [RedPajama-INCITE-Chat-3B-v1](togethercomputer/RedPajama-INCITE-Chat-3B-v1)
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## Description
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This is [RedPajama-INCITE-Chat-3B-v1](togethercomputer/RedPajama-INCITE-Chat-3B-v1) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2024/documentation/openvino-ir-format.html) (Intermediate Representation) format.
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## Compatibility
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The provided OpenVINO™ IR model is compatible with:
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* OpenVINO version 2024.
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* Optimum Intel 1.
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## Running Model Inference with [Optimum Intel](https://huggingface.co/docs/optimum/intel/index)
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1. Install packages required for using [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) integration with the OpenVINO backend:
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For more examples and possible optimizations, refer to the [OpenVINO Large Language Model Inference Guide](https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html).
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## Running Model Inference with [OpenVINO GenAI](https://github.com/openvinotoolkit/openvino.genai)
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1. Install packages required for using OpenVINO GenAI.
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```
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pip install openvino-genai huggingface_hub
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```
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2. Download model from HuggingFace Hub
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```
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import huggingface_hub as hf_hub
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model_id = "OpenVINO/RedPajama-INCITE-Chat-3B-v1-int8-ov"
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model_path = "RedPajama-INCITE-Chat-3B-v1-int8-ov"
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hf_hub.snapshot_download(model_id, local_dir=model_path)
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```
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3. Run model inference:
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```
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import openvino_genai as ov_genai
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device = "CPU"
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pipe = ov_genai.LLMPipeline(model_path, device)
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print(pipe.generate("What is OpenVINO?", max_length=200))
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```
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More GenAI usage examples can be found in OpenVINO GenAI library [docs](https://github.com/openvinotoolkit/openvino.genai/blob/master/src/README.md) and [samples](https://github.com/openvinotoolkit/openvino.genai?tab=readme-ov-file#openvino-genai-samples)
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## Limitations
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Check the original model card for [
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## Legal information
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The original model is distributed under [apache-2.0](https://choosealicense.com/licenses/apache-2.0/) license. More details can be found in [original model card](togethercomputer/RedPajama-INCITE-Chat-3B-v1).
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## Disclaimer
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Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See [Intel’s Global Human Rights Principles](https://www.intel.com/content/dam/www/central-libraries/us/en/documents/policy-human-rights.pdf). Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.
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license_link: https://choosealicense.com/licenses/apache-2.0/
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---
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# RedPajama-INCITE-Chat-3B-v1-int8-ov
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* Model creator: [Togethercomputer](https://huggingface.co/togethercomputer)
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* Original model: [RedPajama-INCITE-Chat-3B-v1](https://huggingface.co/togethercomputer/RedPajama-INCITE-Chat-3B-v1)
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## Description
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This is [RedPajama-INCITE-Chat-3B-v1](https://huggingface.co/togethercomputer/RedPajama-INCITE-Chat-3B-v1) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2024/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to INT8 by [NNCF](https://github.com/openvinotoolkit/nncf).
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## Quantization Parameters
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Weight compression was performed using `nncf.compress_weights` with the following parameters:
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* mode: **int8_asym**
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* ratio: **1**
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For more information on quantization, check the [OpenVINO model optimization guide](https://docs.openvino.ai/2024/openvino-workflow/model-optimization-guide/weight-compression.html).
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## Compatibility
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The provided OpenVINO™ IR model is compatible with:
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* OpenVINO version 2024.4.0 and higher
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* Optimum Intel 1.20.0 and higher
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## Running Model Inference
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1. Install packages required for using [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) integration with the OpenVINO backend:
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For more examples and possible optimizations, refer to the [OpenVINO Large Language Model Inference Guide](https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html).
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## Limitations
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Check the original model card for [original model card](https://huggingface.co/togethercomputer/RedPajama-INCITE-Chat-3B-v1) for limitations.
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## Legal information
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The original model is distributed under [apache-2.0](https://choosealicense.com/licenses/apache-2.0/) license. More details can be found in [original model card](https://huggingface.co/togethercomputer/RedPajama-INCITE-Chat-3B-v1).
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## Disclaimer
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Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See [Intel’s Global Human Rights Principles](https://www.intel.com/content/dam/www/central-libraries/us/en/documents/policy-human-rights.pdf). Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.
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config.json
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{
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"_name_or_path": "
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"architectures": [
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"GPTNeoXForCausalLM"
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"model_type": "gpt_neox",
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"rotary_pct": 1.0,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.45.2",
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"use_cache": true,
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"use_parallel_residual": false,
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"vocab_size": 50432
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generation_config.json
CHANGED
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openvino_detokenizer.bin
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size 514079
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openvino_detokenizer.xml
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@@ -600,22 +429,31 @@
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@@ -631,13 +469,13 @@
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@@ -651,7 +489,7 @@
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@@ -667,15 +505,15 @@
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@@ -711,7 +549,7 @@
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@@ -727,13 +565,13 @@
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@@ -745,14 +583,14 @@
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@@ -777,7 +615,7 @@
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1 |
<?xml version="1.0"?>
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14 |
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|
47 |
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48 |
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49 |
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58 |
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63 |
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64 |
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72 |
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91 |
</port>
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93 |
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96 |
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102 |
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103 |
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105 |
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106 |
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113 |
<port id="2" precision="I64" />
|
114 |
</output>
|
115 |
</layer>
|
116 |
+
<layer id="12" name="Constant_61970" type="Const" version="opset1">
|
117 |
<data element_type="i64" shape="" offset="8" size="8" />
|
118 |
<output>
|
119 |
<port id="0" precision="I64" />
|
120 |
</output>
|
121 |
</layer>
|
122 |
+
<layer id="13" name="Range_61971" type="Range" version="opset4">
|
123 |
<data output_type="i32" />
|
124 |
<input>
|
125 |
<port id="0" precision="I64" />
|
|
|
132 |
</port>
|
133 |
</output>
|
134 |
</layer>
|
135 |
+
<layer id="14" name="Constant_62033" type="Const" version="opset1">
|
136 |
+
<data element_type="u8" shape="704" offset="16" size="704" />
|
137 |
<output>
|
138 |
<port id="0" precision="U8">
|
139 |
+
<dim>704</dim>
|
140 |
</port>
|
141 |
</output>
|
142 |
</layer>
|
143 |
+
<layer id="15" name="SpecialTokensSplit_62034" type="SpecialTokensSplit" version="extension">
|
|
|
144 |
<input>
|
145 |
<port id="0" precision="I32">
|
146 |
<dim>-1</dim>
|
|
|
158 |
<dim>-1</dim>
|
159 |
</port>
|
160 |
<port id="5" precision="U8">
|
161 |
+
<dim>704</dim>
|
162 |
</port>
|
163 |
</input>
|
164 |
<output>
|
|
|
177 |
<port id="10" precision="U8">
|
178 |
<dim>-1</dim>
|
179 |
</port>
|
180 |
+
<port id="11" precision="BOOL">
|
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|
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|
181 |
<dim>-1</dim>
|
182 |
</port>
|
183 |
</output>
|
184 |
</layer>
|
185 |
+
<layer id="16" name="NormalizeUnicode_62035" type="NormalizeUnicode" version="extension">
|
186 |
+
<data normalization_form="NFC" />
|
187 |
<input>
|
188 |
<port id="0" precision="I32">
|
189 |
<dim>-1</dim>
|
|
|
191 |
<port id="1" precision="I32">
|
192 |
<dim>-1</dim>
|
193 |
</port>
|
194 |
+
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|
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|
195 |
<dim>-1</dim>
|
196 |
</port>
|
197 |
+
<port id="3" precision="BOOL">
|
198 |
<dim>-1</dim>
|
199 |
</port>
|
200 |
</input>
|
201 |
<output>
|
202 |
+
<port id="4" precision="I32">
|
203 |
<dim>-1</dim>
|
204 |
</port>
|
205 |
+
<port id="5" precision="I32">
|
206 |
<dim>-1</dim>
|
207 |
</port>
|
208 |
+
<port id="6" precision="U8">
|
209 |
<dim>-1</dim>
|
210 |
</port>
|
211 |
+
<port id="7" precision="BOOL">
|
212 |
<dim>-1</dim>
|
213 |
</port>
|
214 |
+
</output>
|
215 |
+
</layer>
|
216 |
+
<layer id="17" name="Constant_62037" type="Const" version="opset1">
|
217 |
+
<data element_type="u8" shape="64" offset="720" size="64" />
|
218 |
+
<output>
|
219 |
+
<port id="0" precision="U8">
|
220 |
+
<dim>64</dim>
|
221 |
</port>
|
222 |
</output>
|
223 |
</layer>
|
224 |
+
<layer id="18" name="RegexSplit_62038" type="RegexSplit" version="extension">
|
225 |
+
<data behaviour="isolate" invert="false" max_splits="-1" />
|
226 |
<input>
|
227 |
<port id="0" precision="I32">
|
228 |
<dim>-1</dim>
|
|
|
239 |
<port id="4" precision="U8">
|
240 |
<dim>-1</dim>
|
241 |
</port>
|
242 |
+
<port id="5" precision="BOOL">
|
243 |
+
<dim>-1</dim>
|
244 |
+
</port>
|
245 |
+
<port id="6" precision="U8">
|
246 |
+
<dim>64</dim>
|
247 |
+
</port>
|
248 |
</input>
|
249 |
<output>
|
250 |
+
<port id="7" precision="I32">
|
251 |
<dim>-1</dim>
|
252 |
</port>
|
253 |
+
<port id="8" precision="I32">
|
254 |
<dim>-1</dim>
|
255 |
</port>
|
256 |
+
<port id="9" precision="I32">
|
257 |
<dim>-1</dim>
|
258 |
</port>
|
259 |
+
<port id="10" precision="I32">
|
260 |
<dim>-1</dim>
|
261 |
</port>
|
262 |
+
<port id="11" precision="U8">
|
263 |
+
<dim>-1</dim>
|
264 |
+
</port>
|
265 |
+
<port id="12" precision="BOOL">
|
266 |
<dim>-1</dim>
|
267 |
</port>
|
268 |
</output>
|
269 |
</layer>
|
270 |
+
<layer id="19" name="Constant_62040" type="Const" version="opset1">
|
271 |
+
<data element_type="u8" shape="514030" offset="784" size="514030" />
|
272 |
<output>
|
273 |
<port id="0" precision="U8">
|
274 |
+
<dim>514030</dim>
|
275 |
</port>
|
276 |
</output>
|
277 |
</layer>
|
278 |
+
<layer id="20" name="StringTensorUnpack_62041" type="StringTensorUnpack" version="extension">
|
279 |
<data mode="begins_ends" />
|
280 |
<input>
|
281 |
<port id="0" precision="U8">
|
282 |
+
<dim>514030</dim>
|
283 |
</port>
|
284 |
</input>
|
285 |
<output>
|
|
|
294 |
</port>
|
295 |
</output>
|
296 |
</layer>
|
297 |
+
<layer id="21" name="Constant_62046" type="Const" version="opset1">
|
298 |
+
<data element_type="u8" shape="362936" offset="514814" size="362936" />
|
299 |
<output>
|
300 |
<port id="0" precision="U8">
|
301 |
+
<dim>362936</dim>
|
302 |
</port>
|
303 |
</output>
|
304 |
</layer>
|
305 |
+
<layer id="22" name="StringTensorUnpack_62047" type="StringTensorUnpack" version="extension">
|
306 |
<data mode="begins_ends" />
|
307 |
<input>
|
308 |
<port id="0" precision="U8">
|
309 |
+
<dim>362936</dim>
|
310 |
</port>
|
311 |
</input>
|
312 |
<output>
|
|
|
321 |
</port>
|
322 |
</output>
|
323 |
</layer>
|
324 |
+
<layer id="23" name="Constant_62049" type="Const" version="opset1">
|
325 |
+
<data element_type="u8" shape="349500" offset="877750" size="349500" />
|
|
|
|
|
|
|
|
|
|
|
|
|
326 |
<output>
|
327 |
<port id="0" precision="U8">
|
328 |
+
<dim>349500</dim>
|
329 |
</port>
|
330 |
</output>
|
331 |
</layer>
|
332 |
+
<layer id="24" name="StringTensorUnpack_62050" type="StringTensorUnpack" version="extension">
|
333 |
<data mode="begins_ends" />
|
334 |
<input>
|
335 |
<port id="0" precision="U8">
|
336 |
+
<dim>349500</dim>
|
337 |
</port>
|
338 |
</input>
|
339 |
<output>
|
|
|
348 |
</port>
|
349 |
</output>
|
350 |
</layer>
|
351 |
+
<layer id="25" name="Constant_62043" type="Const" version="opset1">
|
352 |
+
<data element_type="u8" shape="414" offset="1227250" size="414" />
|
|
|
|
|
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|
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|
|
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|
353 |
<output>
|
354 |
+
<port id="0" precision="U8">
|
355 |
+
<dim>414</dim>
|
|
|
|
|
|
|
|
|
|
|
|
|
356 |
</port>
|
|
|
|
|
|
|
|
|
|
|
357 |
</output>
|
358 |
</layer>
|
359 |
+
<layer id="26" name="StringTensorUnpack_62044" type="StringTensorUnpack" version="extension">
|
360 |
+
<data mode="begins_ends" />
|
|
|
|
|
|
|
|
|
|
|
|
|
361 |
<input>
|
362 |
+
<port id="0" precision="U8">
|
363 |
+
<dim>414</dim>
|
|
|
|
|
|
|
|
|
|
|
364 |
</port>
|
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|
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|
|
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|
|
|
|
|
|
|
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|
|
|
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|
365 |
</input>
|
366 |
<output>
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
367 |
<port id="1" precision="I32">
|
368 |
<dim>-1</dim>
|
369 |
</port>
|
370 |
<port id="2" precision="I32">
|
371 |
<dim>-1</dim>
|
372 |
</port>
|
373 |
+
<port id="3" precision="U8">
|
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|
|
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|
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|
|
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|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
374 |
<dim>-1</dim>
|
375 |
</port>
|
376 |
</output>
|
377 |
</layer>
|
378 |
+
<layer id="27" name="Constant_62051" type="Const" version="opset1">
|
379 |
+
<data element_type="i32" shape="24" offset="1227664" size="96" />
|
380 |
<output>
|
381 |
<port id="0" precision="I32">
|
382 |
+
<dim>24</dim>
|
383 |
</port>
|
384 |
</output>
|
385 |
</layer>
|
386 |
+
<layer id="28" name="BPETokenizer_62052" type="BPETokenizer" version="extension">
|
387 |
+
<data unk_token="" fuse_unk="false" suffix_indicator="" end_suffix="" byte_fallback="false" cache_capacity="20000" />
|
388 |
<input>
|
389 |
<port id="0" precision="I32">
|
390 |
<dim>-1</dim>
|
|
|
429 |
<dim>-1</dim>
|
430 |
</port>
|
431 |
<port id="14" precision="I32">
|
432 |
+
<dim>-1</dim>
|
433 |
</port>
|
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|
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|
434 |
<port id="15" precision="I32">
|
435 |
<dim>-1</dim>
|
436 |
</port>
|
437 |
+
<port id="16" precision="U8">
|
438 |
<dim>-1</dim>
|
439 |
</port>
|
440 |
<port id="17" precision="I32">
|
441 |
+
<dim>24</dim>
|
442 |
+
</port>
|
443 |
+
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|
444 |
+
<output>
|
445 |
+
<port id="18" precision="I32">
|
446 |
+
<dim>-1</dim>
|
447 |
+
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|
448 |
+
<port id="19" precision="I32">
|
449 |
+
<dim>-1</dim>
|
450 |
+
</port>
|
451 |
+
<port id="20" precision="I32">
|
452 |
<dim>-1</dim>
|
453 |
</port>
|
454 |
</output>
|
455 |
</layer>
|
456 |
+
<layer id="29" name="Subtract_62053" type="Subtract" version="opset1">
|
457 |
<data auto_broadcast="numpy" />
|
458 |
<input>
|
459 |
<port id="0" precision="I32">
|
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|
469 |
</port>
|
470 |
</output>
|
471 |
</layer>
|
472 |
+
<layer id="30" name="Constant_62054" type="Const" version="opset1">
|
473 |
+
<data element_type="i32" shape="" offset="1227760" size="4" />
|
474 |
<output>
|
475 |
<port id="0" precision="I32" />
|
476 |
</output>
|
477 |
</layer>
|
478 |
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<layer id="31" name="Minimum_62055" type="Minimum" version="opset1">
|
479 |
<data auto_broadcast="numpy" />
|
480 |
<input>
|
481 |
<port id="0" precision="I32">
|
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|
489 |
</port>
|
490 |
</output>
|
491 |
</layer>
|
492 |
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<layer id="32" name="Subtract_62056" type="Subtract" version="opset1">
|
493 |
<data auto_broadcast="numpy" />
|
494 |
<input>
|
495 |
<port id="0" precision="I32">
|
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|
505 |
</port>
|
506 |
</output>
|
507 |
</layer>
|
508 |
+
<layer id="33" name="Constant_62057" type="Const" version="opset1">
|
509 |
+
<data element_type="i32" shape="1" offset="1227764" size="4" />
|
510 |
<output>
|
511 |
<port id="0" precision="I32">
|
512 |
<dim>1</dim>
|
513 |
</port>
|
514 |
</output>
|
515 |
</layer>
|
516 |
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<layer id="34" name="CombineSegments_62058" type="CombineSegments" version="extension">
|
517 |
<input>
|
518 |
<port id="0" precision="I32">
|
519 |
<dim>-1</dim>
|
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|
549 |
</port>
|
550 |
</output>
|
551 |
</layer>
|
552 |
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<layer id="35" name="Subtract_62059" type="Subtract" version="opset1">
|
553 |
<data auto_broadcast="numpy" />
|
554 |
<input>
|
555 |
<port id="0" precision="I32">
|
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|
565 |
</port>
|
566 |
</output>
|
567 |
</layer>
|
568 |
+
<layer id="36" name="Constant_62060" type="Const" version="opset1">
|
569 |
+
<data element_type="i32" shape="" offset="1227764" size="4" />
|
570 |
<output>
|
571 |
<port id="0" precision="I32" />
|
572 |
</output>
|
573 |
</layer>
|
574 |
+
<layer id="37" name="ReduceMax_62061" type="ReduceMax" version="opset1">
|
575 |
<data keep_dims="false" />
|
576 |
<input>
|
577 |
<port id="0" precision="I32">
|
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|
583 |
<port id="2" precision="I32" />
|
584 |
</output>
|
585 |
</layer>
|
586 |
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<layer id="38" name="Constant_62062" type="Const" version="opset1">
|
587 |
+
<data element_type="i32" shape="" offset="1227764" size="4" />
|
588 |
<output>
|
589 |
<port id="0" precision="I32" />
|
590 |
</output>
|
591 |
</layer>
|
592 |
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<layer id="39" name="RaggedToDense_62063" type="RaggedToDense" version="extension">
|
593 |
+
<data pad_right="false" />
|
594 |
<input>
|
595 |
<port id="0" precision="I32">
|
596 |
<dim>-1</dim>
|
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|
615 |
</port>
|
616 |
</output>
|
617 |
</layer>
|
618 |
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<layer id="40" name="Convert_62064" type="Convert" version="opset1">
|
619 |
<data destination_type="i32" />
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620 |
<input>
|
621 |
<port id="0" precision="BOOL">
|
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|
630 |
</port>
|
631 |
</output>
|
632 |
</layer>
|
633 |
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<layer id="41" name="Convert_62064" type="Convert" version="opset1">
|
634 |
<data destination_type="i64" />
|
635 |
<input>
|
636 |
<port id="0" precision="I32">
|
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|
645 |
</port>
|
646 |
</output>
|
647 |
</layer>
|
648 |
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<layer id="43" name="RaggedToDense_62063.0" type="Convert" version="opset1">
|
649 |
<data destination_type="i64" />
|
650 |
<input>
|
651 |
<port id="0" precision="I32">
|
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|
660 |
</port>
|
661 |
</output>
|
662 |
</layer>
|
663 |
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<layer id="44" name="Result_62067" type="Result" version="opset1">
|
664 |
<input>
|
665 |
<port id="0" precision="I64">
|
666 |
<dim>-1</dim>
|
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|
668 |
</port>
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669 |
</input>
|
670 |
</layer>
|
671 |
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<layer id="42" name="Result_62069" type="Result" version="opset1">
|
672 |
<input>
|
673 |
<port id="0" precision="I64">
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674 |
<dim>-1</dim>
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|
679 |
</layers>
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680 |
<edges>
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681 |
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682 |
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683 |
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685 |
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688 |
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694 |
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695 |
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698 |
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716 |
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717 |
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718 |
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720 |
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721 |
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723 |
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728 |
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732 |
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742 |
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|
743 |
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744 |
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|
745 |
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<edge from-layer="34" from-port="4" to-layer="39" to-port="0" />
|
746 |
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|
747 |
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<edge from-layer="34" from-port="5" to-layer="35" to-port="0" />
|
748 |
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|
749 |
<edge from-layer="36" from-port="0" to-layer="37" to-port="1" />
|
750 |
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|
751 |
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|
752 |
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|
753 |
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<edge from-layer="39" from-port="5" to-layer="43" to-port="0" />
|
754 |
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<edge from-layer="40" from-port="1" to-layer="41" to-port="0" />
|
755 |
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<edge from-layer="41" from-port="1" to-layer="42" to-port="0" />
|
756 |
+
<edge from-layer="43" from-port="1" to-layer="44" to-port="0" />
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
757 |
</edges>
|
758 |
<rt_info>
|
759 |
+
<bos_token_id value="0" />
|
760 |
<eos_token_id value="0" />
|
761 |
+
<original_tokenizer_class value="<class 'transformers.models.gpt_neox.tokenization_gpt_neox_fast.GPTNeoXTokenizerFast'>" />
|
762 |
</rt_info>
|
763 |
</net>
|
tokenizer.json
CHANGED
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|
|