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Llama-3-8B-INT4-Instruct-OpenVino

OpenVINO
llama
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  1. config.json +28 -0
  2. generation_config.json +12 -0
  3. openvino_detokenizer.bin +3 -0
  4. openvino_detokenizer.xml +222 -0
  5. openvino_model.bin +3 -0
  6. openvino_model.xml +0 -0
  7. openvino_tokenizer.bin +3 -0
  8. openvino_tokenizer.xml +946 -0
  9. special_tokens_map.json +16 -0
  10. tokenizer.json +0 -0
  11. tokenizer_config.json +2062 -0
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+ }
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+ </layer>
665
+ <layer id="49" name="Constant_221029" type="Const" version="opset1">
666
+ <data element_type="i32" shape="2" offset="5689137" size="8" />
667
+ <output>
668
+ <port id="0" precision="I32">
669
+ <dim>2</dim>
670
+ </port>
671
+ </output>
672
+ </layer>
673
+ <layer id="50" name="CombineSegments_221030" type="CombineSegments" version="extension">
674
+ <input>
675
+ <port id="0" precision="I32" />
676
+ <port id="1" precision="I32" />
677
+ <port id="2" precision="I32">
678
+ <dim>1</dim>
679
+ </port>
680
+ <port id="3" precision="I32">
681
+ <dim>-1</dim>
682
+ </port>
683
+ <port id="4" precision="I32">
684
+ <dim>-1</dim>
685
+ </port>
686
+ <port id="5" precision="I32">
687
+ <dim>-1</dim>
688
+ </port>
689
+ <port id="6" precision="I32">
690
+ <dim>2</dim>
691
+ </port>
692
+ </input>
693
+ <output>
694
+ <port id="7" precision="I32">
695
+ <dim>-1</dim>
696
+ </port>
697
+ <port id="8" precision="I32">
698
+ <dim>-1</dim>
699
+ </port>
700
+ <port id="9" precision="I32">
701
+ <dim>-1</dim>
702
+ </port>
703
+ <port id="10" precision="I32">
704
+ <dim>-1</dim>
705
+ </port>
706
+ <port id="11" precision="I32">
707
+ <dim>-1</dim>
708
+ </port>
709
+ <port id="12" precision="I32">
710
+ <dim>-1</dim>
711
+ </port>
712
+ </output>
713
+ </layer>
714
+ <layer id="51" name="Subtract_221031" type="Subtract" version="opset1">
715
+ <data auto_broadcast="numpy" />
716
+ <input>
717
+ <port id="0" precision="I32">
718
+ <dim>-1</dim>
719
+ </port>
720
+ <port id="1" precision="I32">
721
+ <dim>-1</dim>
722
+ </port>
723
+ </input>
724
+ <output>
725
+ <port id="2" precision="I32">
726
+ <dim>-1</dim>
727
+ </port>
728
+ </output>
729
+ </layer>
730
+ <layer id="52" name="Constant_221032" type="Const" version="opset1">
731
+ <data element_type="i32" shape="" offset="0" size="4" />
732
+ <output>
733
+ <port id="0" precision="I32" />
734
+ </output>
735
+ </layer>
736
+ <layer id="53" name="ReduceMax_221033" type="ReduceMax" version="opset1">
737
+ <data keep_dims="false" />
738
+ <input>
739
+ <port id="0" precision="I32">
740
+ <dim>-1</dim>
741
+ </port>
742
+ <port id="1" precision="I32" />
743
+ </input>
744
+ <output>
745
+ <port id="2" precision="I32" />
746
+ </output>
747
+ </layer>
748
+ <layer id="54" name="Constant_221034" type="Const" version="opset1">
749
+ <data element_type="i32" shape="" offset="0" size="4" />
750
+ <output>
751
+ <port id="0" precision="I32" />
752
+ </output>
753
+ </layer>
754
+ <layer id="55" name="RaggedToDense_221035" type="RaggedToDense" version="extension">
755
+ <data pad_right="true" />
756
+ <input>
757
+ <port id="0" precision="I32">
758
+ <dim>-1</dim>
759
+ </port>
760
+ <port id="1" precision="I32">
761
+ <dim>-1</dim>
762
+ </port>
763
+ <port id="2" precision="I32">
764
+ <dim>-1</dim>
765
+ </port>
766
+ <port id="3" precision="I32" />
767
+ <port id="4" precision="I32" />
768
+ </input>
769
+ <output>
770
+ <port id="5" precision="I32">
771
+ <dim>-1</dim>
772
+ <dim>-1</dim>
773
+ </port>
774
+ <port id="6" precision="BOOL">
775
+ <dim>-1</dim>
776
+ <dim>-1</dim>
777
+ </port>
778
+ </output>
779
+ </layer>
780
+ <layer id="56" name="Convert_221036" type="Convert" version="opset1">
781
+ <data destination_type="i32" />
782
+ <input>
783
+ <port id="0" precision="BOOL">
784
+ <dim>-1</dim>
785
+ <dim>-1</dim>
786
+ </port>
787
+ </input>
788
+ <output>
789
+ <port id="1" precision="I32">
790
+ <dim>-1</dim>
791
+ <dim>-1</dim>
792
+ </port>
793
+ </output>
794
+ </layer>
795
+ <layer id="57" name="Convert_221036" type="Convert" version="opset1">
796
+ <data destination_type="i64" />
797
+ <input>
798
+ <port id="0" precision="I32">
799
+ <dim>-1</dim>
800
+ <dim>-1</dim>
801
+ </port>
802
+ </input>
803
+ <output>
804
+ <port id="1" precision="I64" names="attention_mask">
805
+ <dim>-1</dim>
806
+ <dim>-1</dim>
807
+ </port>
808
+ </output>
809
+ </layer>
810
+ <layer id="59" name="RaggedToDense_221035.0" type="Convert" version="opset1">
811
+ <data destination_type="i64" />
812
+ <input>
813
+ <port id="0" precision="I32">
814
+ <dim>-1</dim>
815
+ <dim>-1</dim>
816
+ </port>
817
+ </input>
818
+ <output>
819
+ <port id="1" precision="I64" names="input_ids">
820
+ <dim>-1</dim>
821
+ <dim>-1</dim>
822
+ </port>
823
+ </output>
824
+ </layer>
825
+ <layer id="60" name="Result_221039" type="Result" version="opset1">
826
+ <input>
827
+ <port id="0" precision="I64">
828
+ <dim>-1</dim>
829
+ <dim>-1</dim>
830
+ </port>
831
+ </input>
832
+ </layer>
833
+ <layer id="58" name="Result_221041" type="Result" version="opset1">
834
+ <input>
835
+ <port id="0" precision="I64">
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+ <dim>-1</dim>
837
+ <dim>-1</dim>
838
+ </port>
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+ </input>
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+ </layer>
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+ </layers>
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+ <edges>
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+ <edge from-layer="1" from-port="0" to-layer="50" to-port="0" />
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917
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918
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919
+ <edge from-layer="44" from-port="17" to-layer="50" to-port="5" />
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921
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922
+ <edge from-layer="47" from-port="2" to-layer="48" to-port="1" />
923
+ <edge from-layer="48" from-port="2" to-layer="50" to-port="4" />
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925
+ <edge from-layer="50" from-port="8" to-layer="51" to-port="0" />
926
+ <edge from-layer="50" from-port="7" to-layer="51" to-port="1" />
927
+ <edge from-layer="50" from-port="7" to-layer="55" to-port="0" />
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+ <edge from-layer="50" from-port="8" to-layer="55" to-port="1" />
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+ <edge from-layer="50" from-port="9" to-layer="55" to-port="2" />
930
+ <edge from-layer="51" from-port="2" to-layer="53" to-port="0" />
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+ <edge from-layer="52" from-port="0" to-layer="53" to-port="1" />
932
+ <edge from-layer="53" from-port="2" to-layer="55" to-port="3" />
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+ <edge from-layer="54" from-port="0" to-layer="55" to-port="4" />
934
+ <edge from-layer="55" from-port="6" to-layer="56" to-port="0" />
935
+ <edge from-layer="55" from-port="5" to-layer="59" to-port="0" />
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+ <edge from-layer="56" from-port="1" to-layer="57" to-port="0" />
937
+ <edge from-layer="57" from-port="1" to-layer="58" to-port="0" />
938
+ <edge from-layer="59" from-port="1" to-layer="60" to-port="0" />
939
+ </edges>
940
+ <rt_info>
941
+ <bos_token_id value="128000" />
942
+ <chat_template value="{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '&lt;|start_header_id|>' + message['role'] + '&lt;|end_header_id|>&#10;&#10;'+ message['content'] | trim + '&lt;|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '&lt;|start_header_id|>assistant&lt;|end_header_id|>&#10;&#10;' }}{% endif %}" />
943
+ <eos_token_id value="128009" />
944
+ <original_tokenizer_class value="&lt;class 'transformers.tokenization_utils_fast.PreTrainedTokenizerFast'>" />
945
+ </rt_info>
946
+ </net>
special_tokens_map.json ADDED
@@ -0,0 +1,16 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "content": "<|begin_of_text|>",
4
+ "lstrip": false,
5
+ "normalized": false,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "eos_token": {
10
+ "content": "<|eot_id|>",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ }
16
+ }
tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
tokenizer_config.json ADDED
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1996
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+ }
2051
+ },
2052
+ "bos_token": "<|begin_of_text|>",
2053
+ "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}",
2054
+ "clean_up_tokenization_spaces": true,
2055
+ "eos_token": "<|eot_id|>",
2056
+ "model_input_names": [
2057
+ "input_ids",
2058
+ "attention_mask"
2059
+ ],
2060
+ "model_max_length": 1000000000000000019884624838656,
2061
+ "tokenizer_class": "PreTrainedTokenizerFast"
2062
+ }