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Upload folder using huggingface_hub
Browse files- README.md +19 -51
- config.json +5 -3
- generation_config.json +1 -1
- openvino_detokenizer.bin +2 -2
- openvino_detokenizer.xml +23 -29
- openvino_model.bin +2 -2
- openvino_model.xml +0 -0
- openvino_tokenizer.bin +2 -2
- openvino_tokenizer.xml +181 -362
- tokenization_codegen25.py +1 -1
- tokenizer_config.json +1 -1
README.md
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---
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license: apache-2.0
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- en
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---
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# codegen25-7b-multi-int8-ov
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*
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* Original model: [CodeGen2.5-7B-multi](https://huggingface.co/Salesforce/codegen25-7b-multi_P)
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## Description
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This is [CodeGen2.5-7B-multi](https://huggingface.co/Salesforce/codegen25-7b-multi_P) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2024/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to FP16.
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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: **
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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.
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* Optimum Intel 1.
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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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```
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pip install optimum[openvino]
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```
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2. Run model inference:
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from optimum.intel.openvino import OVModelForCausalLM
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model_id = "OpenVINO/codegen25-7b-multi-int8-ov"
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tokenizer = AutoTokenizer.from_pretrained(model_id
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model = OVModelForCausalLM.from_pretrained(model_id)
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text = "def hello_world():"
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input_ids = tokenizer(text, return_tensors="pt").input_ids
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generated_ids = model.generate(input_ids, max_length=128)
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print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
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```
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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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```
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import huggingface_hub as hf_hub
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model_id = "OpenVINO/codegen25-7b-multi-int8-ov"
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model_path = "codegen25-7b-multi-int8-ov"
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hf_hub.snapshot_download(model_id, local_dir=model_path)
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```
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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("def hello_world():", 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 [
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## Disclaimer
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---
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license: apache-2.0
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license_link: https://choosealicense.com/licenses/apache-2.0/
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---
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# codegen25-7b-multi-int8-ov
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* Model creator: [Salesforce](https://huggingface.co/Salesforce)
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* Original model: [codegen25-7b-multi](https://huggingface.co/Salesforce/codegen25-7b-multi)
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## Description
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This is [codegen25-7b-multi](https://huggingface.co/Salesforce/codegen25-7b-multi) 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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```
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pip install optimum[openvino]
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```
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2. Run model inference:
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from optimum.intel.openvino import OVModelForCausalLM
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model_id = "OpenVINO/codegen25-7b-multi-int8-ov"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = OVModelForCausalLM.from_pretrained(model_id)
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inputs = tokenizer("What is OpenVINO?", return_tensors="pt")
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outputs = model.generate(**inputs, max_length=200)
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text = tokenizer.batch_decode(outputs)[0]
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print(text)
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```
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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/Salesforce/codegen25-7b-multi) 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/Salesforce/codegen25-7b-multi).
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## Disclaimer
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config.json
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{
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"_name_or_path": "Salesforce/codegen25-7b-
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 50256,
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"eos_token_id": 50256,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 11008,
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"max_position_embeddings": 2048,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"transformers_version": "4.
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"use_cache": true,
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"vocab_size": 51200
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}
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{
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"_name_or_path": "Salesforce/codegen25-7b-multi",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 50256,
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"eos_token_id": 50256,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 11008,
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"max_position_embeddings": 2048,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"transformers_version": "4.45.2",
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"use_cache": true,
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"vocab_size": 51200
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}
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generation_config.json
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"_from_model_config": true,
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"bos_token_id": 50256,
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"eos_token_id": 50256,
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"transformers_version": "4.
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"_from_model_config": true,
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"bos_token_id": 50256,
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"eos_token_id": 50256,
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"transformers_version": "4.45.2"
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}
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openvino_detokenizer.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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size 535202
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openvino_detokenizer.xml
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<?xml version="1.0"?>
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|
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_62122" 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_62184" type="Const" version="opset1">
|
136 |
+
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|
137 |
<output>
|
138 |
<port id="0" precision="U8">
|
139 |
+
<dim>19</dim>
|
140 |
</port>
|
141 |
</output>
|
142 |
</layer>
|
143 |
+
<layer id="15" name="SpecialTokensSplit_62185" 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>19</dim>
|
162 |
</port>
|
163 |
</input>
|
164 |
<output>
|
|
|
177 |
<port id="10" precision="U8">
|
178 |
<dim>-1</dim>
|
179 |
</port>
|
180 |
+
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|
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|
181 |
<dim>-1</dim>
|
182 |
</port>
|
183 |
</output>
|
184 |
</layer>
|
185 |
+
<layer id="16" name="NormalizeUnicode_62186" type="NormalizeUnicode" version="extension">
|
186 |
+
<data normalization_form="NFC" />
|
187 |
<input>
|
188 |
<port id="0" precision="I32">
|
189 |
<dim>-1</dim>
|
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|
191 |
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|
192 |
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|
193 |
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|
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 |
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|
203 |
<dim>-1</dim>
|
204 |
</port>
|
205 |
+
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|
206 |
<dim>-1</dim>
|
207 |
</port>
|
208 |
+
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|
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_62188" type="Const" version="opset1">
|
217 |
+
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|
218 |
+
<output>
|
219 |
+
<port id="0" precision="U8">
|
220 |
+
<dim>79</dim>
|
221 |
</port>
|
222 |
</output>
|
223 |
</layer>
|
224 |
+
<layer id="18" name="RegexSplit_62189" type="RegexSplit" version="extension">
|
225 |
+
<data behaviour="contiguous" 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>79</dim>
|
247 |
+
</port>
|
248 |
</input>
|
249 |
<output>
|
250 |
+
<port id="7" precision="I32">
|
251 |
<dim>-1</dim>
|
252 |
</port>
|
253 |
+
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|
254 |
<dim>-1</dim>
|
255 |
</port>
|
256 |
+
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|
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_62191" type="Const" version="opset1">
|
271 |
+
<data element_type="u8" shape="535153" offset="114" size="535153" />
|
272 |
<output>
|
273 |
<port id="0" precision="U8">
|
274 |
+
<dim>535153</dim>
|
275 |
</port>
|
276 |
</output>
|
277 |
</layer>
|
278 |
+
<layer id="20" name="StringTensorUnpack_62192" type="StringTensorUnpack" version="extension">
|
279 |
<data mode="begins_ends" />
|
280 |
<input>
|
281 |
<port id="0" precision="U8">
|
282 |
+
<dim>535153</dim>
|
283 |
</port>
|
284 |
</input>
|
285 |
<output>
|
|
|
294 |
</port>
|
295 |
</output>
|
296 |
</layer>
|
297 |
+
<layer id="21" name="Constant_62197" type="Const" version="opset1">
|
298 |
+
<data element_type="u8" shape="367533" offset="535267" size="367533" />
|
299 |
<output>
|
300 |
<port id="0" precision="U8">
|
301 |
+
<dim>367533</dim>
|
302 |
</port>
|
303 |
</output>
|
304 |
</layer>
|
305 |
+
<layer id="22" name="StringTensorUnpack_62198" type="StringTensorUnpack" version="extension">
|
306 |
<data mode="begins_ends" />
|
307 |
<input>
|
308 |
<port id="0" precision="U8">
|
309 |
+
<dim>367533</dim>
|
310 |
</port>
|
311 |
</input>
|
312 |
<output>
|
|
|
321 |
</port>
|
322 |
</output>
|
323 |
</layer>
|
324 |
+
<layer id="23" name="Constant_62200" type="Const" version="opset1">
|
325 |
+
<data element_type="u8" shape="353061" offset="902800" size="353061" />
|
|
|
|
|
|
|
|
|
|
|
|
|
326 |
<output>
|
327 |
<port id="0" precision="U8">
|
328 |
+
<dim>353061</dim>
|
329 |
</port>
|
330 |
</output>
|
331 |
</layer>
|
332 |
+
<layer id="24" name="StringTensorUnpack_62201" type="StringTensorUnpack" version="extension">
|
333 |
<data mode="begins_ends" />
|
334 |
<input>
|
335 |
<port id="0" precision="U8">
|
336 |
+
<dim>353061</dim>
|
337 |
</port>
|
338 |
</input>
|
339 |
<output>
|
|
|
348 |
</port>
|
349 |
</output>
|
350 |
</layer>
|
351 |
+
<layer id="25" name="Constant_62194" type="Const" version="opset1">
|
352 |
+
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|
|
|
|
|
|
|
|
|
|
|
353 |
<output>
|
354 |
+
<port id="0" precision="U8">
|
355 |
+
<dim>13303</dim>
|
|
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|
356 |
</port>
|
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|
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|
|
|
|
|
|
357 |
</output>
|
358 |
</layer>
|
359 |
+
<layer id="26" name="StringTensorUnpack_62195" type="StringTensorUnpack" version="extension">
|
360 |
+
<data mode="begins_ends" />
|
361 |
<input>
|
362 |
+
<port id="0" precision="U8">
|
363 |
+
<dim>13303</dim>
|
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|
364 |
</port>
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|
365 |
</input>
|
366 |
<output>
|
|
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|
|
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|
|
|
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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 |
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|
374 |
<dim>-1</dim>
|
375 |
</port>
|
376 |
</output>
|
377 |
</layer>
|
378 |
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|
379 |
+
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|
380 |
<output>
|
381 |
<port id="0" precision="I32">
|
382 |
<dim>942</dim>
|
383 |
</port>
|
384 |
</output>
|
385 |
</layer>
|
386 |
+
<layer id="28" name="BPETokenizer_62203" 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>
|
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|
429 |
<dim>-1</dim>
|
430 |
</port>
|
431 |
<port id="14" precision="I32">
|
432 |
+
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|
433 |
+
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|
434 |
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|
435 |
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|
436 |
+
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|
437 |
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|
438 |
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|
439 |
+
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|
440 |
+
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|
441 |
<dim>942</dim>
|
442 |
</port>
|
443 |
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|
444 |
<output>
|
445 |
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|
446 |
<dim>-1</dim>
|
447 |
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|
448 |
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|
449 |
<dim>-1</dim>
|
450 |
</port>
|
451 |
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|
452 |
<dim>-1</dim>
|
453 |
</port>
|
454 |
</output>
|
455 |
</layer>
|
456 |
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<layer id="29" name="Subtract_62204" 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 |
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<layer id="30" name="Constant_62205" type="Const" version="opset1">
|
473 |
+
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|
474 |
<output>
|
475 |
<port id="0" precision="I32" />
|
476 |
</output>
|
477 |
</layer>
|
478 |
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<layer id="31" name="Minimum_62206" 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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|
493 |
<data auto_broadcast="numpy" />
|
494 |
<input>
|
495 |
<port id="0" precision="I32">
|
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|
505 |
</port>
|
506 |
</output>
|
507 |
</layer>
|
508 |
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<layer id="33" name="Subtract_62208" type="Subtract" version="opset1">
|
509 |
<data auto_broadcast="numpy" />
|
510 |
<input>
|
511 |
<port id="0" precision="I32">
|
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|
521 |
</port>
|
522 |
</output>
|
523 |
</layer>
|
524 |
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|
525 |
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|
526 |
<output>
|
527 |
<port id="0" precision="I32" />
|
528 |
</output>
|
529 |
</layer>
|
530 |
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<layer id="35" name="ReduceMax_62210" type="ReduceMax" version="opset1">
|
531 |
<data keep_dims="false" />
|
532 |
<input>
|
533 |
<port id="0" precision="I32">
|
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|
539 |
<port id="2" precision="I32" />
|
540 |
</output>
|
541 |
</layer>
|
542 |
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|
543 |
+
<data element_type="i32" shape="" offset="1272936" size="4" />
|
544 |
<output>
|
545 |
<port id="0" precision="I32" />
|
546 |
</output>
|
547 |
</layer>
|
548 |
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<layer id="37" name="RaggedToDense_62212" type="RaggedToDense" version="extension">
|
549 |
+
<data pad_right="false" />
|
550 |
<input>
|
551 |
<port id="0" precision="I32">
|
552 |
<dim>-1</dim>
|
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|
571 |
</port>
|
572 |
</output>
|
573 |
</layer>
|
574 |
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|
575 |
<data destination_type="i32" />
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576 |
<input>
|
577 |
<port id="0" precision="BOOL">
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|
586 |
</port>
|
587 |
</output>
|
588 |
</layer>
|
589 |
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|
590 |
<data destination_type="i64" />
|
591 |
<input>
|
592 |
<port id="0" precision="I32">
|
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|
601 |
</port>
|
602 |
</output>
|
603 |
</layer>
|
604 |
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|
605 |
<data destination_type="i64" />
|
606 |
<input>
|
607 |
<port id="0" precision="I32">
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|
616 |
</port>
|
617 |
</output>
|
618 |
</layer>
|
619 |
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|
620 |
<input>
|
621 |
<port id="0" precision="I64">
|
622 |
<dim>-1</dim>
|
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|
624 |
</port>
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625 |
</input>
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626 |
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627 |
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628 |
<input>
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629 |
<port id="0" precision="I64">
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630 |
<dim>-1</dim>
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635 |
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664 |
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<edge from-layer="16" from-port="6" to-layer="18" to-port="4" />
|
665 |
+
<edge from-layer="16" from-port="7" to-layer="18" to-port="5" />
|
666 |
+
<edge from-layer="17" from-port="0" to-layer="18" to-port="6" />
|
667 |
+
<edge from-layer="18" from-port="11" to-layer="28" to-port="4" />
|
668 |
+
<edge from-layer="18" from-port="10" to-layer="28" to-port="3" />
|
669 |
+
<edge from-layer="18" from-port="9" to-layer="28" to-port="2" />
|
670 |
+
<edge from-layer="18" from-port="8" to-layer="28" to-port="1" />
|
671 |
+
<edge from-layer="18" from-port="7" to-layer="28" to-port="0" />
|
672 |
+
<edge from-layer="19" from-port="0" to-layer="20" to-port="0" />
|
673 |
+
<edge from-layer="20" from-port="1" to-layer="28" to-port="5" />
|
674 |
+
<edge from-layer="20" from-port="2" to-layer="28" to-port="6" />
|
675 |
+
<edge from-layer="20" from-port="3" to-layer="28" to-port="7" />
|
676 |
+
<edge from-layer="21" from-port="0" to-layer="22" to-port="0" />
|
677 |
+
<edge from-layer="22" from-port="1" to-layer="28" to-port="8" />
|
678 |
+
<edge from-layer="22" from-port="2" to-layer="28" to-port="9" />
|
679 |
+
<edge from-layer="22" from-port="3" to-layer="28" to-port="10" />
|
680 |
+
<edge from-layer="23" from-port="0" to-layer="24" to-port="0" />
|
681 |
+
<edge from-layer="24" from-port="1" to-layer="28" to-port="11" />
|
682 |
+
<edge from-layer="24" from-port="2" to-layer="28" to-port="12" />
|
683 |
+
<edge from-layer="24" from-port="3" to-layer="28" to-port="13" />
|
684 |
+
<edge from-layer="25" from-port="0" to-layer="26" to-port="0" />
|
685 |
+
<edge from-layer="26" from-port="1" to-layer="28" to-port="14" />
|
686 |
+
<edge from-layer="26" from-port="2" to-layer="28" to-port="15" />
|
687 |
+
<edge from-layer="26" from-port="3" to-layer="28" to-port="16" />
|
688 |
+
<edge from-layer="27" from-port="0" to-layer="28" to-port="17" />
|
689 |
+
<edge from-layer="28" from-port="20" to-layer="37" to-port="2" />
|
690 |
+
<edge from-layer="28" from-port="18" to-layer="29" to-port="1" />
|
691 |
+
<edge from-layer="28" from-port="19" to-layer="37" to-port="1" />
|
692 |
+
<edge from-layer="28" from-port="19" to-layer="33" to-port="0" />
|
693 |
+
<edge from-layer="28" from-port="19" to-layer="32" to-port="0" />
|
694 |
+
<edge from-layer="28" from-port="19" to-layer="29" to-port="0" />
|
695 |
+
<edge from-layer="29" from-port="2" to-layer="31" to-port="0" />
|
696 |
+
<edge from-layer="30" from-port="0" to-layer="31" to-port="1" />
|
697 |
+
<edge from-layer="31" from-port="2" to-layer="32" to-port="1" />
|
698 |
+
<edge from-layer="32" from-port="2" to-layer="33" to-port="1" />
|
699 |
+
<edge from-layer="32" from-port="2" to-layer="37" to-port="0" />
|
700 |
+
<edge from-layer="33" from-port="2" to-layer="35" to-port="0" />
|
701 |
+
<edge from-layer="34" from-port="0" to-layer="35" to-port="1" />
|
702 |
+
<edge from-layer="35" from-port="2" to-layer="37" to-port="3" />
|
703 |
+
<edge from-layer="36" from-port="0" to-layer="37" to-port="4" />
|
704 |
+
<edge from-layer="37" from-port="6" to-layer="38" to-port="0" />
|
705 |
+
<edge from-layer="37" from-port="5" to-layer="41" to-port="0" />
|
706 |
+
<edge from-layer="38" from-port="1" to-layer="39" to-port="0" />
|
707 |
+
<edge from-layer="39" from-port="1" to-layer="40" to-port="0" />
|
708 |
+
<edge from-layer="41" from-port="1" to-layer="42" to-port="0" />
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
709 |
</edges>
|
710 |
<rt_info>
|
711 |
<eos_token_id value="50256" />
|
712 |
+
<original_tokenizer_class value="<class 'transformers_modules.pytorch.tokenization_codegen25.CodeGen25Tokenizer'>" />
|
713 |
</rt_info>
|
714 |
</net>
|
tokenization_codegen25.py
CHANGED
@@ -245,4 +245,4 @@ class CodeGen25Tokenizer(PreTrainedTokenizer):
|
|
245 |
|
246 |
# has no vocab file
|
247 |
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None):
|
248 |
-
return ()
|
|
|
245 |
|
246 |
# has no vocab file
|
247 |
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None):
|
248 |
+
return ()
|
tokenizer_config.json
CHANGED
@@ -18,7 +18,7 @@
|
|
18 |
},
|
19 |
"clean_up_tokenization_spaces": true,
|
20 |
"eos_token": "<|endoftext|>",
|
21 |
-
"model_max_length":
|
22 |
"pad_token": null,
|
23 |
"tokenizer_class": "CodeGen25Tokenizer"
|
24 |
}
|
|
|
18 |
},
|
19 |
"clean_up_tokenization_spaces": true,
|
20 |
"eos_token": "<|endoftext|>",
|
21 |
+
"model_max_length": 1000000000000000019884624838656,
|
22 |
"pad_token": null,
|
23 |
"tokenizer_class": "CodeGen25Tokenizer"
|
24 |
}
|