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EXAONE AI Model License Agreement 1.1 - NC
|
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|
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This License Agreement (“Agreement”) is entered into between you (“Licensee”) and LG Management Development
|
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Institute Co., Ltd. (“Licensor”), governing the use of the EXAONE AI Model (“Model”). By downloading,
|
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installing, copying, or using the Model, you agree to comply with and be bound by the terms of this Agreement.
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If you do not agree to all the terms, you must not download, install, copy, or use the Model. This Agreement
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constitutes a binding legal agreement between the Licensee and Licensor.
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1. Definitions
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1.1 Model: The artificial intelligence model provided by Licensor, which includes any software,
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algorithms, machine learning models, or related components supplied by Licensor. This definition extends
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to encompass all updates, enhancements, improvements, bug fixes, patches, or other modifications that may
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be provided by Licensor from time to time, whether automatically or manually implemented.
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1.2 Derivatives: Any modifications, alterations, enhancements, improvements, adaptations, or derivative
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works of the Model created by Licensee or any third party. This includes changes made to the Model's
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architecture, parameters, data processing methods, or any other aspect of the Model that results in a
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modification of its functionality or output.
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1.3 Output: Any data, results, content, predictions, analyses, insights, or other materials generated by
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1.4 Licensor: LG Management Development Institute Co., Ltd., the owner, developer, and provider of the
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EXAONE AI Model. The Licensor holds all rights, title, and interest in the Model and is responsible for
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granting licenses to use the Model under the terms specified in this Agreement.
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1.5 Licensee: The individual, organization, corporation, academic institution, government agency, or other
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entity using or intending to use the Model under the terms and conditions of this Agreement. The Licensee
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is responsible for ensuring compliance with the Agreement by all authorized users who access or utilize
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the Model on behalf of the Licensee.
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2. License Grant
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2.1 Grant of License: Subject to the terms and conditions outlined in this Agreement, the Licensor hereby
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grants the Licensee a limited, non-exclusive, non-transferable, worldwide, and revocable license to:
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a. Access, download, install, and use the Model solely for research purposes. This includes
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evaluation, testing, academic research, experimentation, and participation in competitions, provided
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that such participation is in a non-commercial context. Notwithstanding Section 3.1, the Licensee may
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only provide the Model or Derivatives for a competition if no commercial license is granted to the
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competition organizer or any third party.
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b. Publicly disclose research results and findings derived from the use of the Model or Derivatives,
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including publishing papers or presentations.
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c. Modify the Model and create Derivatives based on the Model, provided that such modifications and
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Derivatives are used exclusively for research purposes. The Licensee may conduct experiments, perform
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analyses, and apply custom modifications to the Model to explore its capabilities and performance
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under various scenarios. If the Model is modified, the modified Model must include “EXAONE” at the
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d. Distribute the Model and Derivatives in each case with a copy of this Agreement.
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2.2 Scope of License: The license granted herein does not authorize the Licensee to use the Model for any
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purpose not explicitly permitted under this Agreement. Any use beyond the scope of this license, including
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any commercial application or external distribution, is strictly prohibited unless explicitly agreed upon
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in writing by the Licensor.
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3. Restrictions
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3.1 Commercial Use: The Licensee is expressly prohibited from using the Model, Derivatives, or Output for
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any commercial purposes, including but not limited to, developing or deploying products, services, or
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applications that generate revenue, whether directly or indirectly. Any commercial exploitation of the
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Model or its derivatives requires a separate commercial license agreement with the Licensor. Furthermore,
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the Licensee shall not use the Model, Derivatives or Output to develop or improve other models.
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3.2 Reverse Engineering: The Licensee shall not decompile, disassemble, reverse engineer, or attempt to
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derive the source code, underlying ideas, algorithms, or structure of the Model, except to the extent that
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such activities are expressly permitted by applicable law. Any attempt to bypass or circumvent
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technological protection measures applied to the Model is strictly prohibited.
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3.3 Unlawful Use: The Licensee shall not use the Model and Derivatives for any illegal, fraudulent, or
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unauthorized activities, nor for any purpose that violates applicable laws or regulations. This includes
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but is not limited to the creation, distribution, or dissemination of malicious, deceptive, or unlawful
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content.
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3.4 Ethical Use: The Licensee shall ensure that the Model or Derivatives is used in an ethical and
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responsible manner, adhering to the following guidelines:
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a. The Model and Derivatives shall not be used to generate, propagate, or amplify false, misleading,
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or harmful information, including fake news, misinformation, or disinformation.
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b. The Model and Derivatives shall not be employed to create, distribute, or promote content that is
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c. The Model and Derivatives shall not infringe on the rights of others, including intellectual
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necessary permissions and consents before using the Model and Derivatives in a manner that may impact
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d. The Model and Derivatives shall not be used in a way that causes harm, whether physical, mental,
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emotional, or financial, to individuals, organizations, or communities. The Licensee shall take all
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reasonable measures to prevent misuse or abuse of the Model and Derivatives that could result in harm
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or injury.
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4. Ownership
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4.1 Intellectual Property: All rights, title, and interest in and to the Model, including any
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modifications, Derivatives, and associated documentation, are and shall remain the exclusive property of
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the Licensor. The Licensee acknowledges that this Agreement does not transfer any ownership rights to the
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Licensee. All trademarks, service marks, and logos associated with the Model are the property of the
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Licensor.
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4.2 Output: All rights, title, and interest in and to the Output generated by the Model and Derivatives
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whether in its original form or modified, are and shall remain the exclusive property of the Licensor.
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Licensee may use, modify, and distribute the Output and its derivatives for research purpose. The Licensee
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shall not claim ownership of the Output except as expressly provided in this Agreement. The Licensee may
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use the Output solely for the purposes permitted under this Agreement and shall not exploit the Output for
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unauthorized or commercial purposes.
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4.3 Attribution: In any publication or presentation of results obtained using the Model, the Licensee
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shall provide appropriate attribution to the Licensor, citing the Model's name and version, along with any
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relevant documentation or references specified by the Licensor.
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5. No Warranty
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5.1 “As-Is” Basis: The Model, Derivatives, and Output are provided on an “as-is” and “as-available” basis,
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without any warranties or representations of any kind, whether express, implied, or statutory. The
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Licensor disclaims all warranties, including but not limited to, implied warranties of merchantability,
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fitness for a particular purpose, accuracy, reliability, non-infringement, or any warranty arising from
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the course of dealing or usage of trade.
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5.2 Performance and Reliability: The Licensor does not warrant or guarantee that the Model, Derivatives or
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Output will meet the Licensee’s requirements, that the operation of the Model, Derivatives or Output will
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be uninterrupted or error-free, or that defects in the Model will be corrected. The Licensee acknowledges
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that the use of the Model, Derivatives or Output is at its own risk and that the Model, Derivatives or
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Output may contain bugs, errors, or other limitations.
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5.3 No Endorsement: The Licensor does not endorse, approve, or certify any results, conclusions, or
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recommendations derived from the use of the Model. The Licensee is solely responsible for evaluating the
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accuracy, reliability, and suitability of the Model for its intended purposes.
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6. Limitation of Liability
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6.1 No Liability for Damages: To the fullest extent permitted by applicable law, in no event shall the
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Licensor be liable for any special, incidental, indirect, consequential, exemplary, or punitive damages,
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including but not limited to, damages for loss of business profits, business interruption, loss of
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business information, loss of data, or any other pecuniary or non-pecuniary loss arising out of or in
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connection with the use or inability to use the Model, Derivatives or any Output, even if the Licensor has
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been advised of the possibility of such damages.
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6.2 Indemnification: The Licensee agrees to indemnify, defend, and hold harmless the Licensor, its
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affiliates, officers, directors, employees, and agents from and against any claims, liabilities, damages,
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losses, costs, or expenses (including reasonable attorneys' fees) arising out of or related to the
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Licensee's use of the Model, any Derivatives, or any Output, including any violation of this Agreement or
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applicable laws.
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7. Termination
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7.1 Termination by Licensor: The Licensor reserves the right to terminate this Agreement and revoke the
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Licensee’s rights to use the Model at any time, with or without cause, and without prior notice if the
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Licensee breaches any of the terms or conditions of this Agreement. Termination shall be effective
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immediately upon notice.
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7.2 Effect of Termination: Upon termination of this Agreement, the Licensee must immediately cease all use
|
||||
of the Model, Derivatives, and Output and destroy all copies of the Model, Derivatives, and Output in its
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possession or control, including any backup or archival copies. The Licensee shall certify in writing to
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the Licensor that such destruction has been completed.
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7.3 Survival: The provisions of this Agreement that by their nature should survive termination, including
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||||
but not limited to, Sections 4 (Ownership), 5 (No Warranty), 6 (Limitation of Liability), and this Section
|
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7 (Termination), shall continue to apply after termination.
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||||
|
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8. Governing Law
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8.1 Governing Law: This Agreement shall be governed by and construed in accordance with the laws of the
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Republic of Korea, without regard to its conflict of laws principles.
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8.2 Arbitration: Any disputes, controversies, or claims arising out of or relating to this Agreement,
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including its existence, validity, interpretation, performance, breach, or termination, shall be referred
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to and finally resolved by arbitration administered by the Korean Commercial Arbitration Board (KCAB) in
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accordance with the International Arbitration Rules of the Korean Commercial Arbitration Board in force at
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the time of the commencement of the arbitration. The seat of arbitration shall be Seoul, Republic of
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Korea. The tribunal shall consist of one arbitrator. The language of the arbitration shall be English.
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|
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9. Alterations
|
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9.1 Modifications: The Licensor reserves the right to modify or amend this Agreement at any time, in its
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sole discretion. Any modifications will be effective upon posting the updated Agreement on the Licensor’s
|
||||
website or through other means of communication. The Licensee is responsible for reviewing the Agreement
|
||||
periodically for changes. Continued use of the Model after any modifications have been made constitutes
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acceptance of the revised Agreement.
|
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9.2 Entire Agreement: This Agreement constitutes the entire agreement between the Licensee and Licensor
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concerning the subject matter hereof and supersedes all prior or contemporaneous oral or written
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agreements, representations, or understandings. Any terms or conditions of any purchase order or other
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document submitted by the Licensee in connection with the Model that are in addition to, different from,
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or inconsistent with the terms and conditions of this Agreement are not binding on the Licensor and are
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void.
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|
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By downloading, installing, or using the EXAONE AI Model, the Licensee acknowledges that it has read,
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understood, and agrees to be bound by the terms and conditions of this Agreement.
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{
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"activation_function": "silu",
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"architectures": [
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"ExaoneForCausalLM"
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],
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"attention_dropout": 0.0,
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"auto_map": {
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"AutoConfig": "configuration_exaone.ExaoneConfig",
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"AutoModelForCausalLM": "modeling_exaone.ExaoneForCausalLM",
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"AutoModelForSequenceClassification": "modeling_exaone.ExaoneForSequenceClassification"
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},
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"bos_token_id": 1,
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"embed_dropout": 0.0,
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"eos_token_id": 361,
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"head_dim": 128,
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"layer_norm_epsilon": 1e-05,
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"max_position_embeddings": 32768,
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"model_type": "exaone",
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"num_attention_heads": 32,
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"num_key_value_heads": 8,
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"num_layers": 32,
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"pad_token_id": 0,
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"rope_scaling": {
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"factor": 8.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_type": "llama3"
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},
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"rope_theta": 1000000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "float32",
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"transformers_version": "4.43.0",
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"use_cache": true,
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"vocab_size": 102400
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}
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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# coding=utf-8
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# Copyright 2021 The LG AI Research EXAONE Lab. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
|
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
|
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
|
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# limitations under the License.
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"""EXAONE model configuration"""
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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EXAONE_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
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class ExaoneConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`ExaoneModel`]. It is used to
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instantiate a EXAONE model according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the EXAONE-3.0-7.8B-Instruct [LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct)
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model
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outputs. Read the documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 102400):
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Vocabulary size of the EXAONE model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`ExaoneModel`]. Vocabulary size of the model.
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Defines the different tokens that can be represented by the `inputs_ids` passed to the forward method of
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[`ExaoneModel`].
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max_position_embeddings (`int`, *optional*, defaults to 2048):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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hidden_size (`int`, *optional*, defaults to 2048):
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Dimensionality of the encoder layers and the pooler layer.
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num_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 32):
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Number of attention heads for each attention layer in the Transformer decoder.
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num_key_value_heads (`int`, *optional*):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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by meanpooling all the original heads within that group. For more details checkout [this
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paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
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`num_attention_heads`.
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intermediate_size (`int`, *optional*, defaults to `hidden_size * 4`):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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activation_function (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the decoder.
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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings.
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rope_scaling (`Dict`, *optional*):
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Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
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and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
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accordingly.
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Expected contents:
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`rope_type` (`str`):
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The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
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'llama3'], with 'default' being the original RoPE implementation.
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`factor` (`float`, *optional*):
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Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
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most scaling types, a `factor` of x will enable the model to handle sequences of length x *
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original maximum pre-trained length.
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`original_max_position_embeddings` (`int`, *optional*):
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Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
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pretraining.
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`attention_factor` (`float`, *optional*):
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Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
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computation. If unspecified, it defaults to value recommended by the implementation, using the
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`factor` field to infer the suggested value.
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`beta_fast` (`float`, *optional*):
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Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
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ramp function. If unspecified, it defaults to 32.
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`beta_slow` (`float`, *optional*):
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Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
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ramp function. If unspecified, it defaults to 1.
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`short_factor` (`List[float]`, *optional*):
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Only used with 'longrope'. The scaling factor to be applied to short contexts (<
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`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
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size divided by the number of attention heads divided by 2
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`long_factor` (`List[float]`, *optional*):
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Only used with 'longrope'. The scaling factor to be applied to long contexts (<
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`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
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size divided by the number of attention heads divided by 2
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`low_freq_factor` (`float`, *optional*):
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Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
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`high_freq_factor` (`float`, *optional*):
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Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
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embed_dropout (`float`, *optional*, defaults to 0.0):
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The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the layer normalization layers.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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relevant if ``config.is_decoder=True``.
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bos_token_id (`int`, *optional*, defaults to 0):
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Beginning of stream token id.
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eos_token_id (`int`, *optional*, defaults to 2):
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End of stream token id.
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Example:
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```python
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>>> from transformers import EXAONEModel, ExaoneConfig
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>>> # Initializing a EXAONE configuration
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>>> configuration = ExaoneConfig()
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>>> # Initializing a model from configuration
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>>> model = EXAONEModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "exaone"
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keys_to_ignore_at_inference = ["past_key_values"]
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attribute_map = {"num_hidden_layers": "num_layers"}
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def __init__(
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self,
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vocab_size=102400,
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max_position_embeddings=2048,
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hidden_size=2048,
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num_layers=32,
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num_attention_heads=32,
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num_key_value_heads=None,
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intermediate_size=None,
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activation_function="silu",
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rope_theta=10000.0,
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rope_scaling=None,
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embed_dropout=0.0,
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attention_dropout=0.0,
|
||||
layer_norm_epsilon=1e-5,
|
||||
initializer_range=0.02,
|
||||
use_cache=True,
|
||||
bos_token_id=0,
|
||||
eos_token_id=2,
|
||||
**kwargs,
|
||||
):
|
||||
self.vocab_size = vocab_size
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.hidden_size = hidden_size
|
||||
self.num_layers = num_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
self.num_layers = num_layers
|
||||
if num_key_value_heads is None:
|
||||
num_key_value_heads = num_attention_heads
|
||||
self.num_key_value_heads = num_key_value_heads
|
||||
if intermediate_size:
|
||||
self.intermediate_size = intermediate_size
|
||||
else:
|
||||
self.intermediate_size = hidden_size * 4
|
||||
self.activation_function = activation_function
|
||||
self.embed_dropout = embed_dropout
|
||||
self.attention_dropout = attention_dropout
|
||||
self.layer_norm_epsilon = layer_norm_epsilon
|
||||
self.initializer_range = initializer_range
|
||||
self.use_cache = use_cache
|
||||
self.rope_theta = rope_theta
|
||||
self.rope_scaling = rope_scaling
|
||||
|
||||
self.bos_token_id = bos_token_id
|
||||
self.eos_token_id = eos_token_id
|
||||
|
||||
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
{
|
||||
"_from_model_config": true,
|
||||
"bos_token_id": 1,
|
||||
"eos_token_id": 361,
|
||||
"pad_token_id": 0,
|
||||
"transformers_version": "4.43.0"
|
||||
}
|
||||
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Reference in New Issue