add data

._____temp/OpenDocVQA-Corpus/.gitattributes

@@ -0,0 +1,59 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
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+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
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+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text

._____temp/OpenDocVQA-Corpus/LICENSE

@@ -0,0 +1,154 @@
+SOFTWARE LICENSE AGREEMENT FOR EVALUATION
+
+This SOFTWARE EVALUATION LICENSE AGREEMENT (this "Agreement") is a legal
+contract between a person who uses or otherwise accesses or installs the
+Software ("User(s)"), and Nippon Telegraph and Telephone corporation ("NTT").
+READ THE TERMS AND CONDITIONS OF THIS AGREEMENT CAREFULLY BEFORE INSTALLING OR
+OTHERWISE ACCESSING OR USING NTT'S PROPRIETARY SOFTWARE ACCOMPANIED BY THIS
+AGREEMENT (the "SOFTWARE"). THE SOFTWARE IS COPYRIGHTED AND IT IS LICENSED TO
+USER UNDER THIS AGREEMENT, NOT SOLD TO USER. BY INSTALLING OR OTHERWISE
+ACCESSING OR USING THE SOFTWARE, USER ACKNOWLEDGES THAT USER HAS READ THIS
+AGREEMENT, THAT USER UNDERSTANDS IT, AND THAT USER ACCEPTS AND AGREES TO BE
+BOUND BY ITS TERMS. IF AT ANY TIME USER IS NOT WILLING TO BE BOUND BY THE
+TERMS OF THIS AGREEMENT, USER SHOULD  TERMINATE THE INSTALLATION PROCESS,
+IMMEDIATELY CEASE AND REFRAIN FROM ACCESSING OR USING THE SOFTWARE AND DELETE
+ANY COPIES USER MAY HAVE. THIS AGREEMENT REPRESENTS THE ENTIRE AGREEMENT
+BETWEEN USER AND NTT CONCERNING THE SOFTWARE.
+ 
+BACKGROUND
+
+A. NTT is the owner of all rights, including all patent rights, copyrights and
+trade secret rights, in and to the Software and related documentation listed
+in Exhibit A to this Agreement.
+
+B. User wishes to obtain a royalty free license to use the Software to enable
+User to evaluate, and NTT wishes to grant such a license to User, pursuant and
+subject to the terms and conditions of this Agreement.
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+C. As a condition to NTT's provision of the Software to User, NTT has required
+User to execute this Agreement.
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+In consideration of these premises, and the mutual promises and conditions in
+this Agreement, the parties hereby agree as follows:
+
+1. Grant of Evaluation License. NTT hereby grants to User, and User hereby
+accepts, under the terms and conditions of this Agreement, a royalty free,
+nontransferable and nonexclusive license to use the Software internally for
+the purposes of testing, analyzing, and evaluating the methods or mechanisms
+as shown in the research paper submitted by NTT to a certain academy. User may
+make a reasonable number of backup copies of the Software solely for User's
+internal use pursuant to the license granted in this Section 1.
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+2. Shipment and Installation. NTT will ship or deliver the Software by any
+method that NTT deems appropriate. User shall be solely responsible for proper
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+3. Term.  This Agreement is effective whichever is earlier (i) upon User's
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+using the Software, even if User has not expressly accepted this Agreement.
+Without prejudice to any other rights, NTT may terminate this Agreement
+without notice to User (i) if User breaches or fails to comply with any of the
+limitations or other requirements described herein, and (ii) if NTT receives a
+notice from the academy stating that the research paper would not be
+published, and in any such case User agrees that NTT may, in addition to any
+other remedies it may have at law or in equity, remotely disable the Software.
+User may terminate this Agreement at any time by User's decision to terminate
+the Agreement to NTT and ceasing use of the Software. Upon any termination or
+expiration of this Agreement for any reason, User agrees to uninstall the
+Software and either return to NTT the Software and all copies thereof, or to
+destroy all such materials and provide written verification of such
+destruction to NTT.
+
+4. Proprietary Rights
+
+(a) The Software is the valuable, confidential, and proprietary property of
+NTT, and NTT shall retain exclusive title to this property both during the
+term and after the termination of this Agreement.  Without limitation, User
+acknowledges that all patent rights, copyrights and trade secret rights in the
+Software shall remain the exclusive property of NTT at all times. User shall
+use not less than reasonable care in safeguarding the confidentiality of the
+Software. 
+
+(b) USER SHALL NOT, IN WHOLE OR IN PART, AT ANY TIME DURING THE TERM OF OR
+AFTER THE TERMINATION OF THIS AGREEMENT: (i) SELL, ASSIGN, LEASE, DISTRIBUTE,
+OR OTHERWISE TRANSFER THE SOFTWARE TO ANY THIRD PARTY; (ii) EXCEPT AS
+OTHERWISE PROVIDED HEREIN, COPY OR REPRODUCE THE SOFTWARE IN ANY MANNER; (iii)
+DISCLOSE THE SOFTWARE TO ANY THIRD PARTY, EXCEPT TO USER'S EMPLOYEES WHO
+REQUIRE ACCESS TO THE SOFTWARE FOR THE PURPOSES OF THIS AGREEMENT; (iv)
+MODIFY, DISASSEMBLE, DECOMPILE, REVERSE ENGINEER OR TRANSLATE THE SOFTWARE; OR
+(v) ALLOW ANY PERSON OR ENTITY TO COMMIT ANY OF THE ACTIONS DESCRIBED IN (i)
+THROUGH (iv) ABOVE.
+
+(c) User shall take appropriate action, by instruction, agreement, or
+otherwise, with respect to its employees permitted under this Agreement to
+have access to the Software to ensure that all of User's obligations under
+this Section 4 shall be satisfied.  
+
+5. Indemnity.  User shall defend, indemnify and hold harmless NTT, its agents
+and employees, from any loss, damage, or liability arising in connection with
+User's improper or unauthorized use of the Software. NTT SHALL HAVE THE SOLE
+RIGHT TO CONDUCT DEFEND ANY ACTTION RELATING TO THE SOFTWARE.
+
+6. Disclaimer.  THE SOFTWARE IS LICENSED TO USER "AS IS," WITHOUT ANY
+TRAINING, MAINTENANCE, OR SERVICE OBLIGATIONS WHATSOEVER ON THE PART OF NTT.
+NTT MAKES NO EXPRESS OR IMPLIED WARRANTIES OF ANY TYPE WHATSOEVER, INCLUDING
+WITHOUT LIMITATION THE IMPLIED WARRANTIES OF MERCHANTABILITY, OF FITNESS FOR A
+PARTICULAR PURPOSE AND OF NON-INFRINGEMENT ON COPYRIGHT OR ANY OTHER RIGHT OF
+THIRD PARTIES.  USER ASSUMES ALL RISKS ASSOCIATED WITH ITS USE OF THE
+SOFTWARE, INCLUDING WITHOUT LIMITATION RISKS RELATING TO QUALITY, PERFORMANCE,
+DATA LOSS, AND UTILITY IN A PRODUCTION ENVIRONMENT. 
+
+7. Limitation of Liability.  IN NO EVENT SHALL NTT BE LIABLE TO USER OR TO ANY
+THIRD PARTY FOR ANY INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES,
+INCLUDING BUT NOT LIMITED TO DAMAGES FOR PERSONAL INJURY, PROPERTY DAMAGE,
+LOST PROFITS, OR OTHER ECONOMIC LOSS, ARISING IN CONNECTION WITH USER'S USE OF
+OR INABILITY TO USE THE SOFTWARE, IN CONNECTION WITH NTT'S PROVISION OF OR
+FAILURE TO PROVIDE SERVICES PERTAINING TO THE SOFTWARE, OR AS A RESULT OF ANY
+DEFECT IN THE SOFTWARE.  THIS DISCLAIMER OF LIABILITY SHALL APPLY REGARD¬LESS
+OF THE FORM OF ACTION THAT MAY BE BROUGHT AGAINST NTT, WHETHER IN CONTRACT OR
+TORT, INCLUDING WITHOUT LIMITATION ANY ACTION FOR NEGLIGENCE.  USER'S SOLE
+REMEDY IN THE EVENT OF ANY BREACH OF THIS AGREEMENT BY NTT SHALL BE
+TERMINATION PURSUANT TO SECTION 3.
+
+8. No Assignment or Sublicense.  Neither this Agreement nor any right or
+license under this Agreement, nor the Software, may be sublicensed, assigned,
+or otherwise transferred by User without NTT's prior written consent.
+
+9. General
+
+(a) If any provision, or part of a provision, of this Agreement is or becomes
+illegal, unenforceable, or invalidated, by operation of law or otherwise, that
+provision or part shall to that extent be deemed omitted, and the remainder of
+this Agreement shall remain in full force and effect.
+
+(b) This Agreement is the complete and exclusive statement of the agreement
+between the parties with respect to the subject matter hereof, and supersedes
+all written and oral contracts, proposals, and other communications between
+the parties relating to that subject matter.  
+
+(c) Subject to Section 8, this Agreement shall be binding on, and shall inure
+to the benefit of, the respective successors and assigns of NTT and User.  
+
+(d) If either party to this Agreement initiates a legal action or proceeding
+to enforce or interpret any part of this Agreement, the prevailing party in
+such action shall be entitled to recover, as an element of the costs of such
+action and not as damages, its attorneys' fees and other costs associated with
+such action or proceeding.
+
+(e) This Agreement shall be governed by and interpreted under the laws of
+Japan, without reference to conflicts of law principles. All disputes arising
+out of or in connection with this Agreement shall be finally settled by
+arbitration in Tokyo in accordance with the Commercial Arbitration Rules of
+the Japan Commercial Arbitration Association.  The arbitration shall be
+conducted by three (3) arbitrators and in Japanese. The award rendered by the
+arbitrators shall be final and binding upon the parties. Judgment upon the
+award may be entered in any court having jurisdiction thereof.
+
+(f) NTT shall not be liable to the User or to any third party for any delay or
+failure to perform NTT's obligation set forth under this Agreement due to any
+cause beyond NTT's reasonable control.
+
+EXHIBIT A
+The software and related data include the following files,
+- all (visualmrc, slidevqa)
+- slidevqa

._____temp/OpenDocVQA-Corpus/README.md

@@ -0,0 +1,312 @@
+---
+dataset_info:
+- config_name: all
+  features:
+  - name: doc_id
+    dtype: string
+  - name: image
+    dtype: image
+  - name: dataset_name
+    dtype: string
+  splits:
+  - name: test
+    num_bytes: 33863176699.362
+    num_examples: 206267
+  download_size: 39444733971
+  dataset_size: 33863176699.362
+- config_name: chartqa
+  features:
+  - name: doc_id
+    dtype: string
+  - name: image
+    dtype: image
+  - name: dataset_name
+    dtype: string
+  splits:
+  - name: test
+    num_bytes: 1002602606.782
+    num_examples: 20882
+  download_size: 955114086
+  dataset_size: 1002602606.782
+- config_name: default
+  features:
+  - name: doc_id
+    dtype: string
+  - name: image
+    dtype: image
+  - name: dataset_name
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 1957342799.05
+    num_examples: 12230
+  download_size: 5075559665
+  dataset_size: 1957342799.05
+- config_name: dude
+  features:
+  - name: doc_id
+    dtype: string
+  - name: image
+    dtype: image
+  - name: dataset_name
+    dtype: string
+  splits:
+  - name: test
+    num_bytes: 10850355002.6
+    num_examples: 27955
+  download_size: 9918891043
+  dataset_size: 10850355002.6
+- config_name: infovqa
+  features:
+  - name: doc_id
+    dtype: string
+  - name: image
+    dtype: image
+  - name: dataset_name
+    dtype: string
+  splits:
+  - name: test
+    num_bytes: 2037249851.38
+    num_examples: 5485
+  download_size: 1821864668
+  dataset_size: 2037249851.38
+- config_name: slidevqa
+  features:
+  - name: doc_id
+    dtype: string
+  - name: image
+    dtype: image
+  - name: dataset_name
+    dtype: string
+  splits:
+  - name: test
+    num_bytes: 6852775381.32
+    num_examples: 52380
+  download_size: 6477479204
+  dataset_size: 6852775381.32
+configs:
+- config_name: all
+  data_files:
+  - split: test
+    path: all/test-*
+- config_name: chartqa
+  data_files:
+  - split: test
+    path: chartqa/test-*
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+- config_name: dude
+  data_files:
+  - split: test
+    path: dude/test-*
+- config_name: infovqa
+  data_files:
+  - split: test
+    path: infovqa/test-*
+- config_name: slidevqa
+  data_files:
+  - split: test
+    path: slidevqa/test-*
+task_categories:
+- visual-question-answering
+- question-answering
+language:
+- en
+size_categories:
+- 100K<n<1M
+extra_gated_prompt: "1.The researcher agrees to the following terms and conditions\
+  \ of data sources:\n* DocVQA: https://rrc.cvc.uab.es/?ch=17&com=downloads\n* InfoVQA:\
+  \ https://rrc.cvc.uab.es/?ch=17&com=downloads\n\n2.VisualMRC and SlideVQA datasets\
+  \ should only be used under the following NTT License.\n### SOFTWARE LICENSE AGREEMENT\
+  \ FOR EVALUATION\nThis SOFTWARE EVALUATION LICENSE AGREEMENT (this \"Agreement\"\
+  ) is a legal contract between a person who uses or otherwise accesses or installs\
+  \ the Software (\"User(s)\"), and Nippon Telegraph and Telephone corporation (\"\
+  NTT\"). READ THE TERMS AND CONDITIONS OF THIS AGREEMENT CAREFULLY BEFORE INSTALLING\
+  \ OR OTHERWISE ACCESSING OR USING NTT'S PROPRIETARY SOFTWARE ACCOMPANIED BY THIS\
+  \ AGREEMENT (the \"SOFTWARE\"). THE SOFTWARE IS COPYRIGHTED AND IT IS LICENSED TO\
+  \ USER UNDER THIS AGREEMENT, NOT SOLD TO USER. BY INSTALLING OR OTHERWISE ACCESSING\
+  \ OR USING THE SOFTWARE, USER ACKNOWLEDGES THAT USER HAS READ THIS AGREEMENT, THAT\
+  \ USER UNDERSTANDS IT, AND THAT USER ACCEPTS AND AGREES TO BE BOUND BY ITS TERMS.\
+  \ IF AT ANY TIME USER IS NOT WILLING TO BE BOUND BY THE TERMS OF THIS AGREEMENT,\
+  \ USER SHOULD  TERMINATE THE INSTALLATION PROCESS, IMMEDIATELY CEASE AND REFRAIN\
+  \ FROM ACCESSING OR USING THE SOFTWARE AND DELETE ANY COPIES USER MAY HAVE. THIS\
+  \ AGREEMENT REPRESENTS THE ENTIRE AGREEMENT BETWEEN USER AND NTT CONCERNING THE\
+  \ SOFTWARE.\n \n## BACKGROUND\nA. NTT is the owner of all rights, including all\
+  \ patent rights, copyrights and trade secret rights, in and to the Software and\
+  \ related documentation listed in Exhibit A to this Agreement.\nB. User wishes to\
+  \ obtain a royalty free license to use the Software to enable User to evaluate,\
+  \ and NTT wishes to grant such a license to User, pursuant and subject to the terms\
+  \ and conditions of this Agreement.\nC. As a condition to NTT's provision of the\
+  \ Software to User, NTT has required User to execute this Agreement.\nIn consideration\
+  \ of these premises, and the mutual promises and conditions in this Agreement, the\
+  \ parties hereby agree as follows:\n1. Grant of Evaluation License. NTT hereby grants\
+  \ to User, and User hereby accepts, under the terms and conditions of this Agreement,\
+  \ a royalty free, nontransferable and nonexclusive license to use the Software internally\
+  \ for the purposes of testing, analyzing, and evaluating the methods or mechanisms\
+  \ as shown in the research paper submitted by NTT to a certain academy. User may\
+  \ make a reasonable number of backup copies of the Software solely for User's internal\
+  \ use pursuant to the license granted in this Section 1.\n2. Shipment and Installation.\
+  \ NTT will ship or deliver the Software by any method that NTT deems appropriate.\
+  \ User shall be solely responsible for proper installation of the Software.\n3.\
+  \ Term.  This Agreement is effective whichever is earlier (i) upon User's acceptance\
+  \ of the Agreement, or (ii) upon User's installing, accessing, and using the Software,\
+  \ even if User has not expressly accepted this Agreement. Without prejudice to any\
+  \ other rights, NTT may terminate this Agreement without notice to User (i) if User\
+  \ breaches or fails to comply with any of the limitations or other requirements\
+  \ described herein, and (ii) if NTT receives a notice from the academy stating that\
+  \ the research paper would not be published, and in any such case User agrees that\
+  \ NTT may, in addition to any other remedies it may have at law or in equity, remotely\
+  \ disable the Software. User may terminate this Agreement at any time by User's\
+  \ decision to terminate the Agreement to NTT and ceasing use of the Software. Upon\
+  \ any termination or expiration of this Agreement for any reason, User agrees to\
+  \ uninstall the Software and either return to NTT the Software and all copies thereof,\
+  \ or to destroy all such materials and provide written verification of such destruction\
+  \ to NTT.\n4. Proprietary Rights\n(a) The Software is the valuable, confidential,\
+  \ and proprietary property of NTT, and NTT shall retain exclusive title to this\
+  \ property both during the term and after the termination of this Agreement.  Without\
+  \ limitation, User acknowledges that all patent rights, copyrights and trade secret\
+  \ rights in the Software shall remain the exclusive property of NTT at all times.\
+  \ User shall use not less than reasonable care in safeguarding the confidentiality\
+  \ of the Software. \n(b) USER SHALL NOT, IN WHOLE OR IN PART, AT ANY TIME DURING\
+  \ THE TERM OF OR AFTER THE TERMINATION OF THIS AGREEMENT: (i) SELL, ASSIGN, LEASE,\
+  \ DISTRIBUTE, OR OTHERWISE TRANSFER THE SOFTWARE TO ANY THIRD PARTY; (ii) EXCEPT\
+  \ AS OTHERWISE PROVIDED HEREIN, COPY OR REPRODUCE THE SOFTWARE IN ANY MANNER; (iii)\
+  \ DISCLOSE THE SOFTWARE TO ANY THIRD PARTY, EXCEPT TO USER'S EMPLOYEES WHO REQUIRE\
+  \ ACCESS TO THE SOFTWARE FOR THE PURPOSES OF THIS AGREEMENT; (iv) MODIFY, DISASSEMBLE,\
+  \ DECOMPILE, REVERSE ENGINEER OR TRANSLATE THE SOFTWARE; OR (v) ALLOW ANY PERSON\
+  \ OR ENTITY TO COMMIT ANY OF THE ACTIONS DESCRIBED IN (i) THROUGH (iv) ABOVE.\n\
+  (c) User shall take appropriate action, by instruction, agreement, or otherwise,\
+  \ with respect to its employees permitted under this Agreement to have access to\
+  \ the Software to ensure that all of User's obligations under this Section 4 shall\
+  \ be satisfied.  \n5. Indemnity.  User shall defend, indemnify and hold harmless\
+  \ NTT, its agents and employees, from any loss, damage, or liability arising in\
+  \ connection with User's improper or unauthorized use of the Software. NTT SHALL\
+  \ HAVE THE SOLE RIGHT TO CONDUCT DEFEND ANY ACTTION RELATING TO THE SOFTWARE.\n\
+  6. Disclaimer.  THE SOFTWARE IS LICENSED TO USER \"AS IS,\" WITHOUT ANY TRAINING,\
+  \ MAINTENANCE, OR SERVICE OBLIGATIONS WHATSOEVER ON THE PART OF NTT. NTT MAKES NO\
+  \ EXPRESS OR IMPLIED WARRANTIES OF ANY TYPE WHATSOEVER, INCLUDING WITHOUT LIMITATION\
+  \ THE IMPLIED WARRANTIES OF MERCHANTABILITY, OF FITNESS FOR A PARTICULAR PURPOSE\
+  \ AND OF NON-INFRINGEMENT ON COPYRIGHT OR ANY OTHER RIGHT OF THIRD PARTIES.  USER\
+  \ ASSUMES ALL RISKS ASSOCIATED WITH ITS USE OF THE SOFTWARE, INCLUDING WITHOUT LIMITATION\
+  \ RISKS RELATING TO QUALITY, PERFORMANCE, DATA LOSS, AND UTILITY IN A PRODUCTION\
+  \ ENVIRONMENT. \n7. Limitation of Liability.  IN NO EVENT SHALL NTT BE LIABLE TO\
+  \ USER OR TO ANY THIRD PARTY FOR ANY INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL\
+  \ DAMAGES, INCLUDING BUT NOT LIMITED TO DAMAGES FOR PERSONAL INJURY, PROPERTY DAMAGE,\
+  \ LOST PROFITS, OR OTHER ECONOMIC LOSS, ARISING IN CONNECTION WITH USER'S USE OF\
+  \ OR INABILITY TO USE THE SOFTWARE, IN CONNECTION WITH NTT'S PROVISION OF OR FAILURE\
+  \ TO PROVIDE SERVICES PERTAINING TO THE SOFTWARE, OR AS A RESULT OF ANY DEFECT IN\
+  \ THE SOFTWARE.  THIS DISCLAIMER OF LIABILITY SHALL APPLY REGARD¬LESS OF THE FORM\
+  \ OF ACTION THAT MAY BE BROUGHT AGAINST NTT, WHETHER IN CONTRACT OR TORT, INCLUDING\
+  \ WITHOUT LIMITATION ANY ACTION FOR NEGLIGENCE.  USER'S SOLE REMEDY IN THE EVENT\
+  \ OF ANY BREACH OF THIS AGREEMENT BY NTT SHALL BE TERMINATION PURSUANT TO SECTION\
+  \ 3.\n8. No Assignment or Sublicense.  Neither this Agreement nor any right or license\
+  \ under this Agreement, nor the Software, may be sublicensed, assigned, or otherwise\
+  \ transferred by User without NTT's prior written consent.\n9. General\n(a) If any\
+  \ provision, or part of a provision, of this Agreement is or becomes illegal, unenforceable,\
+  \ or invalidated, by operation of law or otherwise, that provision or part shall\
+  \ to that extent be deemed omitted, and the remainder of this Agreement shall remain\
+  \ in full force and effect.\n(b) This Agreement is the complete and exclusive statement\
+  \ of the agreement between the parties with respect to the subject matter hereof,\
+  \ and supersedes all written and oral contracts, proposals, and other communications\
+  \ between the parties relating to that subject matter.  \n(c) Subject to Section\
+  \ 8, this Agreement shall be binding on, and shall inure to the benefit of, the\
+  \ respective successors and assigns of NTT and User.  \n(d) If either party to this\
+  \ Agreement initiates a legal action or proceeding to enforce or interpret any part\
+  \ of this Agreement, the prevailing party in such action shall be entitled to recover,\
+  \ as an element of the costs of such action and not as damages, its attorneys' fees\
+  \ and other costs associated with such action or proceeding.\n(e) This Agreement\
+  \ shall be governed by and interpreted under the laws of Japan, without reference\
+  \ to conflicts of law principles. All disputes arising out of or in connection with\
+  \ this Agreement shall be finally settled by arbitration in Tokyo in accordance\
+  \ with the Commercial Arbitration Rules of the Japan Commercial Arbitration Association.\
+  \  The arbitration shall be conducted by three (3) arbitrators and in Japanese.\
+  \ The award rendered by the arbitrators shall be final and binding upon the parties.\
+  \ Judgment upon the award may be entered in any court having jurisdiction thereof.\n\
+  (f) NTT shall not be liable to the User or to any third party for any delay or failure\
+  \ to perform NTT's obligation set forth under this Agreement due to any cause beyond\
+  \ NTT's reasonable control.\nEXHIBIT A\nThe software and related data include the\
+  \ following files,\n* data (visualmrc)\n* all(visualmrc, slidevqa)\n* slidevqa"
+extra_gated_fields:
+  By clicking Submit below, I accept the terms of the conditions and license: checkbox
+extra_gated_button_content: Submit
+---
+# Dataset Card for OpenDocVQA
+This is a training and evaluation corpus data file for [VDocRAG](https://vdocrag.github.io/), a new RAG framework that can directly understand diverse real-world documents purely from visual features.
+
+## Dataset Description
+
+OpenDocVQA is the first unified collection of open-domain document visual question answering datasets, encompassing diverse document types and formats.
+
+#### Supported Tasks and Leaderboards
+Given a large collection of document images and a question, the goal of the OpenDocVQA task is to output an answer by finding the relevant images. We decompose the task into two stages. 
+
+- **Visual document retrieval**: Given a question and a corpus of document images, the model retrieves the relevant images from which to derive the answer.
+- **DocumentVQA**: The model takes a question and the retrieved images as input to generate an answer. 
+
+OpenDocVQA covers multiple open-domain DocumentVQA datasets with diverse document types. To reflect real-world scenarios, we evaluate models with both single-pool and all-pool settings. 
+In the single-pool setting, retrieval is performed from a specific pool of documents provided by each original dataset. The all-pool setting requires retrieving from the entire candidate pool, which includes documents from a wide range of domains.
+
+#### Languages
+English
+
+## Dataset Structure
+
+### Data Instances
+```json
+{
+  'doc_id': 'visualmrc/okfn.org/__about__press__releases__open-revolution-rewriting-rules-information-age01.png',
+  'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=715x433 at 0x7F07E2565750>,
+  'dataset_name': 'visualmrc'
+}
+```
+
+### Data Fields
+An example of a sample looks as follows:
+```json
+{
+  'doc_id': Document ID,
+  'image': PIL.Image,
+  'dataset_name': Source dataset name
+}
+```
+
+### Stats about the datasets in The OpenDocVQA
+
+| Dataset              | Documents | # images | # Train&Dev | # Test  |
+|----------------------|-----------|----------|-------------|---------|
+| DocVQA               | Industry   | 12,767    | 6,382        | -        |
+| InfoVQA               | Infographic   | 5,485    | 9,592        | 1,048        |
+| VisualMRC               | Webpage   | 10,229    | 6,126        | -        |
+| ChartQA               | Chart   | 20,882    | -        | 150       |
+| OpenWikitable               | Table   | 1,257    | 4,261        | -        |
+| DUDE               | Open   | 27,955    | 2,135        | 496        |
+| MPMQA               | Manual   | 10,018    | 3,054        | -        |
+| SlideVQA               | Slide   | 52,380    | -        | 760        |
+| MHDocVQA  (newly created)            | Open   | 28,550    | 9,470        | -        |
+
+
+
+## Additional Information
+
+### License
+Each of the publicly available sub-datasets present in the OpenDocVQA is governed by specific licensing conditions. 
+Therefore, when making use of them, you must take into consideration each of the licenses governing each dataset.
+
+The images of VisualMRC and SlideVQA datasets in this repo are released under the [NTT License](https://huggingface.co/NTT-hil-insight/OpenDocVQA/edit/main/LICENSE).
+
+### Citation
+```bibtex
+@inproceedings{tanaka2025vdocrag,
+  author    = {Ryota Tanaka and
+               Taichi Iki and
+               Taku Hasegawa and
+               Kyosuke Nishida and
+               Kuniko Saito and
+               Jun Suzuki},
+  title     = {VDocRAG: Retrieval-Augmented Generation over Visually-Rich Documents},
+  booktitle = {CVPR},
+  year      = {2025}
+}
+```

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+SOFTWARE LICENSE AGREEMENT FOR EVALUATION
+
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+EXHIBIT A
+The software and related data include the following files,
+- data (visualmrc)
+- slidevqa

._____temp/OpenDocVQA/README.md

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+---
+dataset_info:
+- config_name: chartqa
+  features:
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+    dtype: string
+  - name: query
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+  - name: answers
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+  dataset_size: 36856
+- config_name: default
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+  - name: query
+    dtype: string
+  - name: answers
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+  - name: relevant_doc_ids
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+  features:
+  - name: query_id
+    dtype: string
+  - name: query
+    dtype: string
+  - name: answers
+    sequence: string
+  - name: relevant_doc_ids
+    sequence: string
+  - name: dataset_names
+    sequence: string
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+    num_examples: 496
+  download_size: 60971
+  dataset_size: 136528
+- config_name: infovqa
+  features:
+  - name: query_id
+    dtype: string
+  - name: query
+    dtype: string
+  - name: answers
+    sequence: string
+  - name: relevant_doc_ids
+    sequence: string
+  - name: dataset_names
+    sequence: string
+  splits:
+  - name: test
+    num_bytes: 246601
+    num_examples: 1048
+  download_size: 70905
+  dataset_size: 246601
+- config_name: slidevqa
+  features:
+  - name: query_id
+    dtype: string
+  - name: query
+    dtype: string
+  - name: answers
+    sequence: string
+  - name: relevant_doc_ids
+    sequence: string
+  - name: dataset_names
+    sequence: string
+  splits:
+  - name: test
+    num_bytes: 271232
+    num_examples: 760
+  download_size: 78642
+  dataset_size: 271232
+- config_name: arxivqa
+  features:
+  - name: query_id
+    dtype: string
+  - name: query
+    dtype: string
+  - name: answers
+    sequence: string
+  - name: relevant_doc_ids
+    sequence: string
+  - name: dataset_names
+    sequence: string
+  splits:
+  - name: test
+    num_bytes: 501365
+    num_examples: 816
+  download_size: 501365
+  dataset_size: 501365
+- config_name: plotqa
+  features:
+  - name: query_id
+    dtype: string
+  - name: query
+    dtype: string
+  - name: answers
+    sequence: string
+  - name: relevant_doc_ids
+    sequence: string
+  - name: dataset_names
+    sequence: string
+  splits:
+  - name: test
+    num_bytes: 508031
+    num_examples: 863
+  download_size: 508031
+  dataset_size: 508031
+- config_name: docvqa
+  features:
+  - name: query_id
+    dtype: string
+  - name: query
+    dtype: string
+  - name: answers
+    sequence: string
+  - name: relevant_doc_ids
+    sequence: string
+  - name: dataset_names
+    sequence: string
+  splits:
+  - name: test
+    num_bytes: 323886
+    num_examples: 585
+  download_size: 323886
+  dataset_size: 323886
+configs:
+- config_name: chartqa
+  data_files:
+  - split: test
+    path: chartqa/test-*
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+- config_name: dude
+  data_files:
+  - split: test
+    path: dude/test-*
+- config_name: infovqa
+  data_files:
+  - split: test
+    path: infovqa/test-*
+- config_name: slidevqa
+  data_files:
+  - split: test
+    path: slidevqa/test-*
+- config_name: arxivqa
+  data_files:
+  - split: test
+    path: arxivqa/test-*
+- config_name: plotqa
+  data_files:
+  - split: test
+    path: plotqa/test-*
+- config_name: docvqa
+  data_files:
+  - split: test
+    path: docvqa/test-*
+task_categories:
+- visual-question-answering
+- question-answering
+language:
+- en
+size_categories:
+- 100K<n<1M
+extra_gated_prompt: "1.The researcher agrees to the following terms and conditions\
+  \ of data sources:\n* DocVQA: https://rrc.cvc.uab.es/?ch=17&com=downloads\n* InfoVQA:\
+  \ https://rrc.cvc.uab.es/?ch=17&com=downloads\n\n2.VisualMRC and SlideVQA datasets\
+  \ should only be used under the following NTT License.\n### SOFTWARE LICENSE AGREEMENT\
+  \ FOR EVALUATION\nThis SOFTWARE EVALUATION LICENSE AGREEMENT (this \"Agreement\"\
+  ) is a legal contract between a person who uses or otherwise accesses or installs\
+  \ the Software (\"User(s)\"), and Nippon Telegraph and Telephone corporation (\"\
+  NTT\"). READ THE TERMS AND CONDITIONS OF THIS AGREEMENT CAREFULLY BEFORE INSTALLING\
+  \ OR OTHERWISE ACCESSING OR USING NTT'S PROPRIETARY SOFTWARE ACCOMPANIED BY THIS\
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+  6. Disclaimer.  THE SOFTWARE IS LICENSED TO USER \"AS IS,\" WITHOUT ANY TRAINING,\
+  \ MAINTENANCE, OR SERVICE OBLIGATIONS WHATSOEVER ON THE PART OF NTT. NTT MAKES NO\
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+  \ ASSUMES ALL RISKS ASSOCIATED WITH ITS USE OF THE SOFTWARE, INCLUDING WITHOUT LIMITATION\
+  \ RISKS RELATING TO QUALITY, PERFORMANCE, DATA LOSS, AND UTILITY IN A PRODUCTION\
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+  \ USER OR TO ANY THIRD PARTY FOR ANY INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL\
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+  \ LOST PROFITS, OR OTHER ECONOMIC LOSS, ARISING IN CONNECTION WITH USER'S USE OF\
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+  \ TO PROVIDE SERVICES PERTAINING TO THE SOFTWARE, OR AS A RESULT OF ANY DEFECT IN\
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+  \ OF ACTION THAT MAY BE BROUGHT AGAINST NTT, WHETHER IN CONTRACT OR TORT, INCLUDING\
+  \ WITHOUT LIMITATION ANY ACTION FOR NEGLIGENCE.  USER'S SOLE REMEDY IN THE EVENT\
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+  \ under this Agreement, nor the Software, may be sublicensed, assigned, or otherwise\
+  \ transferred by User without NTT's prior written consent.\n9. General\n(a) If any\
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+  \ or invalidated, by operation of law or otherwise, that provision or part shall\
+  \ to that extent be deemed omitted, and the remainder of this Agreement shall remain\
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+  \ of the agreement between the parties with respect to the subject matter hereof,\
+  \ and supersedes all written and oral contracts, proposals, and other communications\
+  \ between the parties relating to that subject matter.  \n(c) Subject to Section\
+  \ 8, this Agreement shall be binding on, and shall inure to the benefit of, the\
+  \ respective successors and assigns of NTT and User.  \n(d) If either party to this\
+  \ Agreement initiates a legal action or proceeding to enforce or interpret any part\
+  \ of this Agreement, the prevailing party in such action shall be entitled to recover,\
+  \ as an element of the costs of such action and not as damages, its attorneys' fees\
+  \ and other costs associated with such action or proceeding.\n(e) This Agreement\
+  \ shall be governed by and interpreted under the laws of Japan, without reference\
+  \ to conflicts of law principles. All disputes arising out of or in connection with\
+  \ this Agreement shall be finally settled by arbitration in Tokyo in accordance\
+  \ with the Commercial Arbitration Rules of the Japan Commercial Arbitration Association.\
+  \  The arbitration shall be conducted by three (3) arbitrators and in Japanese.\
+  \ The award rendered by the arbitrators shall be final and binding upon the parties.\
+  \ Judgment upon the award may be entered in any court having jurisdiction thereof.\n\
+  (f) NTT shall not be liable to the User or to any third party for any delay or failure\
+  \ to perform NTT's obligation set forth under this Agreement due to any cause beyond\
+  \ NTT's reasonable control.\nEXHIBIT A\nThe software and related data include the\
+  \ following files,\n* data (visualmrc, mhdocvqa)\n* slidevqa"
+extra_gated_fields:
+  By clicking Submit below, I accept the terms of the conditions and license: checkbox
+extra_gated_button_content: Submit
+---
+# Dataset Card for OpenDocVQA
+This is a training and evaluation QA data file for [VDocRAG](https://vdocrag.github.io/), a new RAG framework that can directly understand diverse real-world documents purely from visual features.
+
+## Dataset Description
+
+OpenDocVQA is the first unified collection of open-domain document visual question answering datasets, encompassing diverse document types and formats.
+
+#### Supported Tasks and Leaderboards
+Given a large collection of document images and a question, the goal of the OpenDocVQA task is to output an answer by finding the relevant images. We decompose the task into two stages. 
+
+- **Visual document retrieval**: Given a question and a corpus of document images, the model retrieves the relevant images from which to derive the answer.
+- **DocumentVQA**: The model takes a question and the retrieved images as input to generate an answer. 
+
+OpenDocVQA covers multiple open-domain DocumentVQA datasets with diverse document types. To reflect real-world scenarios, we evaluate models with both single-pool and all-pool settings. 
+In the single-pool setting, retrieval is performed from a specific pool of documents provided by each original dataset. The all-pool setting requires retrieving from the entire candidate pool, which includes documents from a wide range of domains.
+
+#### Languages
+English
+
+## Dataset Structure
+
+### Data Instances
+```json
+{
+  'query_id': "chartqa-test_6",
+  'query': 'Instruct: Given a user query, retrieve a chart image that answers the query.\nQuery: How many more Hispanics younger than 18 tend to be Mexican than Spanish?',
+  'answers': ['65'],
+  'relevant_doc_ids': ['chartqa/9001.png'],
+  'dataset_names': ['chartqa']
+}
+```
+
+### Data Fields
+An example of a sample looks as follows:
+```json
+{
+  'query_id': Query ID,
+  'query': Query,
+  'answers': List of answers,
+  'relevant_doc_ids': List of relevant document IDs,
+  'dataset_names': List of source dataset names
+}
+```
+
+### Stats about the datasets in The OpenDocVQA
+
+| Dataset              | Documents | # images | # Train&Dev | # Test  |
+|----------------------|-----------|----------|-------------|---------|
+| DocVQA               | Industry   | 12,767    | 6,382        | -        |
+| InfoVQA               | Infographic   | 5,485    | 9,592        | 1,048        |
+| VisualMRC               | Webpage   | 10,229    | 6,126        | -        |
+| ChartQA               | Chart   | 20,882    | -        | 150       |
+| OpenWikitable               | Table   | 1,257    | 4,261        | -        |
+| DUDE               | Open   | 27,955    | 2,135        | 496        |
+| MPMQA               | Manual   | 10,018    | 3,054        | -        |
+| SlideVQA               | Slide   | 52,380    | -        | 760        |
+| MHDocVQA  (newly created)            | Open   | 28,550    | 9,470        | -        |
+
+
+
+## Additional Information
+
+### License
+Each of the publicly available sub-datasets present in the OpenDocVQA is governed by specific licensing conditions. 
+Therefore, when making use of them, you must take into consideration each of the licenses governing each dataset.
+
+The QA pairs of MHDocVQA, VisualMRC, and SlideVQA datasets in this repo are released under the [NTT License](https://huggingface.co/NTT-hil-insight/OpenDocVQA/edit/main/LICENSE).
+
+### Citation
+```bibtex
+@inproceedings{tanaka2025vdocrag,
+  author    = {Ryota Tanaka and
+               Taichi Iki and
+               Taku Hasegawa and
+               Kyosuke Nishida and
+               Kuniko Saito and
+               Jun Suzuki},
+  title     = {VDocRAG: Retrieval-Augmented Generation over Visually-Rich Documents},
+  booktitle = {CVPR},
+  year      = {2025}
+}
+```

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
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._____temp/Phi3/CODE_OF_CONDUCT.md

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+# Microsoft Open Source Code of Conduct
+
+This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
+
+Resources:
+
+- [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/)
+- [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/)
+- Contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with questions or concerns

._____temp/Phi3/LICENSE

@@ -0,0 +1,21 @@
+    MIT License
+
+    Copyright (c) Microsoft Corporation.
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to deal
+    in the Software without restriction, including without limitation the rights
+    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in all
+    copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+    SOFTWARE

._____temp/Phi3/README.md

@@ -0,0 +1,231 @@
+---
+license: mit
+license_link: https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/resolve/main/LICENSE
+
+language:
+- multilingual
+pipeline_tag: text-generation
+tags:
+- nlp
+- code
+- vision
+inference:
+  parameters:
+    temperature: 0.7
+widget:
+  - messages:
+      - role: user
+        content: <|image_1|>Can you describe what you see in the image?
+---
+🎉 **Phi-3.5**: [[mini-instruct]](https://huggingface.co/microsoft/Phi-3.5-mini-instruct); [[MoE-instruct]](https://huggingface.co/microsoft/Phi-3.5-MoE-instruct) ; [[vision-instruct]](https://huggingface.co/microsoft/Phi-3.5-vision-instruct)
+
+## Model Summary
+
+The Phi-3-Vision-128K-Instruct is a lightweight, state-of-the-art open multimodal model built upon datasets which include - synthetic data and filtered publicly available websites - with a focus on very high-quality, reasoning dense data both on text and vision.  The model belongs to the Phi-3 model family, and the multimodal version comes with 128K context length (in tokens) it can support. The model underwent a rigorous enhancement process, incorporating both supervised fine-tuning and direct preference optimization to ensure precise instruction adherence and robust safety measures.
+
+Resources and Technical Documentation:
+
++ [Phi-3 Microsoft Blog](https://aka.ms/Phi-3Build2024)
++ [Phi-3 Technical Report](https://aka.ms/phi3-tech-report)
++ [Phi-3 on Azure AI Studio](https://aka.ms/try-phi3vision)
++ [Phi-3 Cookbook](https://github.com/microsoft/Phi-3CookBook)
+
+
+|         | Short Context | Long Context |
+| ------- | ------------- | ------------ |
+| Mini    | 4K [[HF]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-onnx) ; [[GGUF]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct-onnx)|
+| Small   | 8K [[HF]](https://huggingface.co/microsoft/Phi-3-small-8k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-small-8k-instruct-onnx-cuda) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-small-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-small-128k-instruct-onnx-cuda)|
+| Medium  | 4K [[HF]](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct-onnx-cuda) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct-onnx-cuda)|
+| Vision  |  | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct-onnx-cuda)|
+
+## Intended Uses
+
+**Primary use cases**
+
+The model is intended for broad commercial and research use in English. The model provides uses for general purpose AI systems and applications with visual and text input capabilities which require 
+
+1) memory/compute constrained environments;
+2) latency bound scenarios;
+3) general image understanding;
+4) OCR;
+5) chart and table understanding.
+
+Our model is designed to accelerate research on efficient language and multimodal models, for use as a building block for generative AI powered features.
+
+**Use case considerations**
+
+Our models are not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fairness before using within a specific downstream use case, particularly for high-risk scenarios. 
+Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case. 
+
+Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under.
+
+## How to Use
+
+Phi-3-Vision-128K-Instruct has been integrated in the development version (4.40.2) of `transformers`. Until the official version is released through `pip`, ensure that you are doing one of the following:
+* When loading the model, ensure that `trust_remote_code=True` is passed as an argument of the `from_pretrained()` function.
+
+* Update your local `transformers` to the development version: `pip uninstall -y transformers && pip install git+https://github.com/huggingface/transformers`. The previous command is an alternative to cloning and installing from the source.
+
+The current `transformers` version can be verified with: `pip list | grep transformers`.
+
+Examples of required packages:
+```
+flash_attn==2.5.8
+numpy==1.24.4
+Pillow==10.3.0
+Requests==2.31.0
+torch==2.3.0
+torchvision==0.18.0
+transformers==4.40.2
+```
+
+Phi-3-Vision-128K-Instruct is also available in [Azure AI Studio](https://aka.ms/phi3-azure-ai).
+
+### Chat Format
+
+Given the nature of the training data, the Phi-3-Vision-128K-Instruct model is best suited for a single image input wih prompts using the chat format as follows. 
+You can provide the prompt as a single image with a generic template as follow:
+```markdown
+<|user|>\n<|image_1|>\n{prompt}<|end|>\n<|assistant|>\n 
+```
+
+where the model generates the text after `<|assistant|>` . In case of multi-turn conversation, the prompt can be formatted as follows:
+
+```markdown
+<|user|>\n<|image_1|>\n{prompt_1}<|end|>\n<|assistant|>\n{response_1}<|end|>\n<|user|>\n{prompt_2}<|end|>\n<|assistant|>\n 
+```
+
+### Sample inference code
+
+This code snippets show how to get quickly started with running the model on a GPU:
+
+```python
+from PIL import Image 
+import requests 
+from transformers import AutoModelForCausalLM 
+from transformers import AutoProcessor 
+
+model_id = "microsoft/Phi-3-vision-128k-instruct" 
+
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda", trust_remote_code=True, torch_dtype="auto", _attn_implementation='flash_attention_2') # use _attn_implementation='eager' to disable flash attention
+
+processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True) 
+
+messages = [ 
+    {"role": "user", "content": "<|image_1|>\nWhat is shown in this image?"}, 
+    {"role": "assistant", "content": "The chart displays the percentage of respondents who agree with various statements about their preparedness for meetings. It shows five categories: 'Having clear and pre-defined goals for meetings', 'Knowing where to find the information I need for a meeting', 'Understanding my exact role and responsibilities when I'm invited', 'Having tools to manage admin tasks like note-taking or summarization', and 'Having more focus time to sufficiently prepare for meetings'. Each category has an associated bar indicating the level of agreement, measured on a scale from 0% to 100%."}, 
+    {"role": "user", "content": "Provide insightful questions to spark discussion."} 
+] 
+
+url = "https://assets-c4akfrf5b4d3f4b7.z01.azurefd.net/assets/2024/04/BMDataViz_661fb89f3845e.png" 
+image = Image.open(requests.get(url, stream=True).raw) 
+
+prompt = processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+inputs = processor(prompt, [image], return_tensors="pt").to("cuda:0") 
+
+generation_args = { 
+    "max_new_tokens": 500, 
+    "temperature": 0.0, 
+    "do_sample": False, 
+} 
+
+generate_ids = model.generate(**inputs, eos_token_id=processor.tokenizer.eos_token_id, **generation_args) 
+
+# remove input tokens 
+generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
+response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] 
+
+print(response) 
+```
+
+Additional basic examples are provided [here](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/blob/main/sample_inference.py).
+
+### How to finetune?
+We recommend user to take a look at the [Phi-3 CookBook finetuning recipe for Vision](https://github.com/microsoft/Phi-3CookBook/blob/main/md/04.Fine-tuning/FineTuning_Vision.md)
+
+
+## Responsible AI Considerations
+
+Like other models, the Phi family of models can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include:   
+
++ Quality of Service: The Phi models are trained primarily on English text. Languages other than English will experience worse performance. English language varieties with less representation in the training data might experience worse performance than standard American English.    
++ Representation of Harms & Perpetuation of Stereotypes: These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases.  
++ Inappropriate or Offensive Content: These models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the use case.  
++ Information Reliability: Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated.   
++ Limited Scope for Code: Majority of Phi-3 training data is based in Python and use common packages such as "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses.      
+
+Developers should apply responsible AI best practices and are responsible for ensuring that a specific use case complies with relevant laws and regulations (e.g. privacy, trade, etc.). Important areas for consideration include:  
+
++ Allocation: Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques.
++ High-Risk Scenarios: Developers should assess suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context.
++ Misinformation: Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG).
++ Generation of Harmful Content: Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case.
++ Misuse: Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations.
++ Identification of individuals: models with vision capabilities may have the potential to uniquely identify individuals in images. Safety post-training steers the model to refuse such requests, but developers should consider and implement, as appropriate, additional mitigations or user consent flows as required in their respective jurisdiction, (e.g., building measures to blur faces in image inputs before processing.
+  
+## Training
+
+### Model
+
+* Architecture: Phi-3-Vision-128K-Instruct has 4.2B parameters and contains image encoder, connector, projector, and Phi-3 Mini language model.
+* Inputs: Text and Image. It’s best suited for prompts using the chat format. 
+* Context length: 128K tokens
+* GPUs: 512 H100-80G
+* Training time: 1.5 days
+* Training data: 500B vision and text tokens
+* Outputs: Generated text in response to the input
+* Dates: Our models were trained between February and April 2024
+* Status: This is a static model trained on an offline text dataset with cutoff date Mar 15, 2024. Future versions of the tuned models may be released as we improve models.
+* Release Type: Open weight release
+* Release dates: The model weight is released on May 21, 2024.
+
+### Datasets
+
+Our training data includes a wide variety of sources, and is a combination of 
+
+1) publicly available documents filtered rigorously for quality, selected high-quality educational data and code;
+2) selected high-quality image-text interleave;
+3) newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.), newly created image data, e.g., chart/table/diagram/slides;
+4) high quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness.
+
+The data collection process involved sourcing information from publicly available documents, with a meticulous approach to filtering out undesirable documents and images. To safeguard privacy, we carefully filtered various image and text data sources to remove or scrub any potentially personal data from the training data.
+ 
+More details can be found in the [Phi-3 Technical Report](https://aka.ms/phi3-tech-report).
+
+## Benchmarks
+
+To understand the capabilities, we compare Phi-3-Vision-128K-Instruct with a set of models over a variety of zero-shot benchmarks using our internal benchmark platform.
+
+|Benchmark|Phi-3 Vision-128K-In|LlaVA-1.6 Vicuna-7B|QWEN-VL Chat|Llama3-Llava-Next-8B|Claude-3 Haiku|Gemini 1.0 Pro V|GPT-4V-Turbo|
+|---------|---------------------|------------------|------------|--------------------|--------------|----------------|------------|
+|MMMU|40.4|34.2|39.0|36.4|40.7|42.0|55.5| 
+|MMBench|80.5|76.3|75.8|79.4|62.4|80.0|86.1|
+|ScienceQA|90.8|70.6|67.2|73.7|72.0|79.7|75.7|
+|MathVista|44.5|31.5|29.4|34.8|33.2|35.0|47.5|
+|InterGPS|38.1|20.5|22.3|24.6|32.1|28.6|41.0|
+|AI2D|76.7|63.1|59.8|66.9|60.3|62.8|74.7|
+|ChartQA|81.4|55.0|50.9|65.8|59.3|58.0|62.3|
+|TextVQA|70.9|64.6|59.4|55.7|62.7|64.7|68.1|
+|POPE|85.8|87.2|82.6|87.0|74.4|84.2|83.7|
+
+
+## Software
+
+* [PyTorch](https://github.com/pytorch/pytorch)
+* [Transformers](https://github.com/huggingface/transformers)
+* [Flash-Attention](https://github.com/HazyResearch/flash-attention)
+
+## Hardware
+Note that by default, the Phi-3-Vision-128K model uses flash attention, which requires certain types of GPU hardware to run. We have tested on the following GPU types:
+* NVIDIA A100
+* NVIDIA A6000
+* NVIDIA H100
+
+## License
+
+The model is licensed under the [MIT license](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/resolve/main/LICENSE).
+
+## Trademarks
+
+This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.

._____temp/Phi3/SECURITY.md

@@ -0,0 +1,41 @@
+<!-- BEGIN MICROSOFT SECURITY.MD V0.0.9 BLOCK -->
+
+## Security
+
+Microsoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet) and [Xamarin](https://github.com/xamarin).
+
+If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://aka.ms/security.md/definition), please report it to us as described below.
+
+## Reporting Security Issues
+
+**Please do not report security vulnerabilities through public GitHub issues.**
+
+Instead, please report them to the Microsoft Security Response Center (MSRC) at [https://msrc.microsoft.com/create-report](https://aka.ms/security.md/msrc/create-report).
+
+If you prefer to submit without logging in, send email to [secure@microsoft.com](mailto:secure@microsoft.com).  If possible, encrypt your message with our PGP key; please download it from the [Microsoft Security Response Center PGP Key page](https://aka.ms/security.md/msrc/pgp).
+
+You should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Additional information can be found at [microsoft.com/msrc](https://www.microsoft.com/msrc). 
+
+Please include the requested information listed below (as much as you can provide) to help us better understand the nature and scope of the possible issue:
+
+  * Type of issue (e.g. buffer overflow, SQL injection, cross-site scripting, etc.)
+  * Full paths of source file(s) related to the manifestation of the issue
+  * The location of the affected source code (tag/branch/commit or direct URL)
+  * Any special configuration required to reproduce the issue
+  * Step-by-step instructions to reproduce the issue
+  * Proof-of-concept or exploit code (if possible)
+  * Impact of the issue, including how an attacker might exploit the issue
+
+This information will help us triage your report more quickly.
+
+If you are reporting for a bug bounty, more complete reports can contribute to a higher bounty award. Please visit our [Microsoft Bug Bounty Program](https://aka.ms/security.md/msrc/bounty) page for more details about our active programs.
+
+## Preferred Languages
+
+We prefer all communications to be in English.
+
+## Policy
+
+Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://aka.ms/security.md/cvd).
+
+<!-- END MICROSOFT SECURITY.MD BLOCK -->

._____temp/Phi3/SUPPORT.md

@@ -0,0 +1,25 @@
+# TODO: The maintainer of this repo has not yet edited this file
+
+**REPO OWNER**: Do you want Customer Service & Support (CSS) support for this product/project?
+
+- **No CSS support:** Fill out this template with information about how to file issues and get help.
+- **Yes CSS support:** Fill out an intake form at [aka.ms/onboardsupport](https://aka.ms/onboardsupport). CSS will work with/help you to determine next steps.
+- **Not sure?** Fill out an intake as though the answer were "Yes". CSS will help you decide.
+
+*Then remove this first heading from this SUPPORT.MD file before publishing your repo.*
+
+# Support
+
+## How to file issues and get help  
+
+This project uses GitHub Issues to track bugs and feature requests. Please search the existing 
+issues before filing new issues to avoid duplicates.  For new issues, file your bug or 
+feature request as a new Issue.
+
+For help and questions about using this project, please **REPO MAINTAINER: INSERT INSTRUCTIONS HERE 
+FOR HOW TO ENGAGE REPO OWNERS OR COMMUNITY FOR HELP. COULD BE A STACK OVERFLOW TAG OR OTHER
+CHANNEL. WHERE WILL YOU HELP PEOPLE?**.
+
+## Microsoft Support Policy  
+
+Support for this **PROJECT or PRODUCT** is limited to the resources listed above.

._____temp/Phi3/config.json

@@ -0,0 +1,148 @@
+{
+  "_name_or_path": "Phi-3-vision-128k-instruct",
+  "architectures": [
+    "Phi3VForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_phi3_v.Phi3VConfig",
+    "AutoModelForCausalLM": "modeling_phi3_v.Phi3VForCausalLM"
+  },
+  "bos_token_id": 1,
+  "embd_layer": {
+    "embedding_cls": "image",
+    "hd_transform_order": "sub_glb",
+    "projection_cls": "mlp",
+    "use_hd_transform": true,
+    "with_learnable_separator": true
+  },
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "img_processor": {
+    "image_dim_out": 1024,
+    "model_name": "openai/clip-vit-large-patch14-336",
+    "name": "clip_vision_model",
+    "num_img_tokens": 144
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "model_type": "phi3_v",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "original_max_position_embeddings": 4096,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "long_factor": [
+      1.0299999713897705,
+      1.0499999523162842,
+      1.0499999523162842,
+      1.0799999237060547,
+      1.2299998998641968,
+      1.2299998998641968,
+      1.2999999523162842,
+      1.4499999284744263,
+      1.5999999046325684,
+      1.6499998569488525,
+      1.8999998569488525,
+      2.859999895095825,
+      3.68999981880188,
+      5.419999599456787,
+      5.489999771118164,
+      5.489999771118164,
+      9.09000015258789,
+      11.579999923706055,
+      15.65999984741211,
+      15.769999504089355,
+      15.789999961853027,
+      18.360000610351562,
+      21.989999771118164,
+      23.079999923706055,
+      30.009998321533203,
+      32.35000228881836,
+      32.590003967285156,
+      35.56000518798828,
+      39.95000457763672,
+      53.840003967285156,
+      56.20000457763672,
+      57.95000457763672,
+      59.29000473022461,
+      59.77000427246094,
+      59.920005798339844,
+      61.190006256103516,
+      61.96000671386719,
+      62.50000762939453,
+      63.3700065612793,
+      63.48000717163086,
+      63.48000717163086,
+      63.66000747680664,
+      63.850006103515625,
+      64.08000946044922,
+      64.760009765625,
+      64.80001068115234,
+      64.81001281738281,
+      64.81001281738281
+    ],
+    "short_factor": [
+      1.05,
+      1.05,
+      1.05,
+      1.1,
+      1.1,
+      1.1,
+      1.2500000000000002,
+      1.2500000000000002,
+      1.4000000000000004,
+      1.4500000000000004,
+      1.5500000000000005,
+      1.8500000000000008,
+      1.9000000000000008,
+      2.000000000000001,
+      2.000000000000001,
+      2.000000000000001,
+      2.000000000000001,
+      2.000000000000001,
+      2.000000000000001,
+      2.000000000000001,
+      2.000000000000001,
+      2.000000000000001,
+      2.000000000000001,
+      2.000000000000001,
+      2.000000000000001,
+      2.000000000000001,
+      2.000000000000001,
+      2.000000000000001,
+      2.000000000000001,
+      2.000000000000001,
+      2.000000000000001,
+      2.000000000000001,
+      2.1000000000000005,
+      2.1000000000000005,
+      2.2,
+      2.3499999999999996,
+      2.3499999999999996,
+      2.3499999999999996,
+      2.3499999999999996,
+      2.3999999999999995,
+      2.3999999999999995,
+      2.6499999999999986,
+      2.6999999999999984,
+      2.8999999999999977,
+      2.9499999999999975,
+      3.049999999999997,
+      3.049999999999997,
+      3.049999999999997
+    ],
+    "type": "su"
+  },
+  "rope_theta": 10000.0,
+  "sliding_window": 131072,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.38.1",
+  "use_cache": true,
+  "vocab_size": 32064,
+  "_attn_implementation": "flash_attention_2"
+}

._____temp/Phi3/configuration_phi3_v.py

@@ -0,0 +1,217 @@
+# coding=utf-8
+# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+""" Phi-3-V model configuration"""
+
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+PHI3V_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "microsoft/Phi-3-vision-128k-instruct": "https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/resolve/main/config.json",
+}
+
+
+class Phi3VConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Phi3VModel`]. It is used to instantiate a Phi-3
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the
+    [microsoft/Phi-3-vision-128k-instruct](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct).
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32064):
+            Vocabulary size of the Phi-3-V model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`Phi3VModel`].
+        hidden_size (`int`, *optional*, defaults to 3072):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 8192):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        resid_pdrop (`float`, *optional*, defaults to 0.0):
+            Dropout probability for mlp outputs.
+        embd_pdrop (`int`, *optional*, defaults to 0.0):
+            The dropout ratio for the embeddings.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio after computing the attention scores.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 4096):
+            The maximum sequence length that this model might ever be used with.
+        original_max_position_embeddings (`int`, *optional*, defaults to 4096):
+            The maximum sequence length that this model was trained with. This is used to determine the size of the
+            original RoPE embeddings when using long scaling.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon value used for the RMSNorm.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`. Whether to tie weight embeddings or not.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`dict`, *optional*):
+            The scaling strategy for the RoPE embeddings. If `None`, no scaling is applied. If a dictionary, it must
+            contain the following keys: `type`, `short_factor` and `long_factor`. The `type` must be either `su` or `yarn` and
+            the `short_factor` and `long_factor` must be lists of numbers with the same length as the hidden size
+            divided by the number of attention heads divided by 2.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            The id of the "beginning-of-sequence" token.
+        eos_token_id (`int`, *optional*, defaults to 32000):
+            The id of the "end-of-sequence" token.
+        pad_token_id (`int`, *optional*, defaults to 32000):
+            The id of the padding token.
+        sliding_window (`int`, *optional*):
+            Sliding window attention window size. If `None`, no sliding window is applied.
+        embd_layer (`str`, *optional*, defaults to `"default"`):
+            The embedding layer to use. Can be either `"default"` or `"image"`. "default" uses the standard embedding for text. 
+
+    Example:
+
+    ```python
+    >>> from transformers import Phi3VModel, Phi3VConfig
+
+    >>> # Initializing a Phi-3-V style configuration
+    >>> configuration = Phi3Config.from_pretrained("microsoft/Phi-3-vision-128k-instruct")
+
+    >>> # Initializing a model from the configuration
+    >>> model = Phi3VModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "phi3_v"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32064,
+        hidden_size=3072,
+        intermediate_size=8192,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        resid_pdrop=0.0,
+        embd_pdrop=0.0,
+        attention_dropout=0.0,
+        hidden_act="silu",
+        max_position_embeddings=4096,
+        original_max_position_embeddings=4096,
+        initializer_range=0.02,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        bos_token_id=1,
+        eos_token_id=32000,
+        pad_token_id=32000,
+        sliding_window=None,
+        embd_layer: str = "default",
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.resid_pdrop = resid_pdrop
+        self.embd_pdrop = embd_pdrop
+        self.attention_dropout = attention_dropout
+        self.hidden_act = hidden_act
+        self.max_position_embeddings = max_position_embeddings
+        self.original_max_position_embeddings = original_max_position_embeddings
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+        self.sliding_window = sliding_window
+        self.embd_layer = embd_layer
+
+
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            pad_token_id=pad_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 3:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with three fields, `type`, `short_factor` and `long_factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_short_factor = self.rope_scaling.get("short_factor", None)
+        rope_scaling_long_factor = self.rope_scaling.get("long_factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["su", "yarn"]:
+            raise ValueError(f"`rope_scaling`'s type field must be one of ['su', 'yarn'], got {rope_scaling_type}")
+        if not (
+            isinstance(rope_scaling_short_factor, list)
+            and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor)
+        ):
+            raise ValueError(
+                f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}"
+            )
+        if not len(rope_scaling_short_factor) == self.hidden_size // self.num_attention_heads // 2:
+            raise ValueError(
+                f"`rope_scaling`'s short_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_short_factor)}"
+            )
+        if not (
+            isinstance(rope_scaling_long_factor, list)
+            and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor)
+        ):
+            raise ValueError(
+                f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}"
+            )
+        if not len(rope_scaling_long_factor) == self.hidden_size // self.num_attention_heads // 2:
+            raise ValueError(
+                f"`rope_scaling`'s long_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_long_factor)}"
+            )