Agree to our license to download PLaMo-100B
To download PLaMo-100B, you have to agree to our license. PLaMo-100B is released under both commercial and non-commercial license. For non-commercial use, please check the LICENSE. For commercial use, please contact us via this form
PLaMo Non-Commercial License Agreement
The PLaMo Non-Commercial License Agreement hereby sets forth the licensing terms and conditions the User must comply with for the non-commercial use of the foundational large language model PLaMo-100B, provided by Preferred Networks, Inc. By agreeing to this Agreement or by using the Model, the User consents to be legally bound by all terms and conditions stipulated herein.
Article 1: Definitions
(1) "Agreement" shall mean this PLaMo Non-Commercial License Agreement.
(2) "PFN" stands for Preferred Networks, Inc.
(3) "Model" shall mean the model code named "PLaMo-100B", including its training scripts, Tokenizer, pre-trained weights, and any associated components or resources provided by PFN.
(4) "User" is the person or legal entity that uses the Model.
(5) "License" shall mean the permission granted by PFN to User to use the Model under the terms of this Agreement.
(6) "Derivative Model" shall mean any model code created through modifications of the Model, such as, fine-tuning, downsizing by quantization, code editing, and parameter tuning. The Derivative Model includes the weights of fine-tuning and other associated components and resources of the created model.
(7) "Outputs" shall mean the results generated by the Model or Derivative Model.
(8) "Models and Outputs" shall collectively refer to the Model, Derivative Models, and Outputs.
Article 2: User
The User must be at least 18 years of age, or of legal age to independently enter into an agreement in their country of residence. Notwithstanding, this requirement does not apply if the User’s parent or legal proxy provides their consent for the User to enter into this Agreement.
Article 3: License
(1) PFN grants the User permission to use the Model under the terms and conditions of this Agreement, and to the extent stipulated herein, provided that the User agrees to and abides by all these terms and conditions.
(2) The License provided shall be non-exclusive, worldwide, revocable, non-sublicensable, non-transferrable, and royalty-free.
(3) The User shall only use the Models and Outputs for personal or academic applications.
(4) The User is prohibited from using the Models and Outputs for any of the following purposes or any other commercial purposes:
(a) For any business of the User or a third party.
(b) For the development or research of models or services intended for commercial applications
(5) The User shall not provide the Model or any Derivative Models to any third parties, nor shall the User allow third parties to use them, regardless of whether the use is for commercial or non-commercial purposes.
Article 4: Derivative Model
(1) The User may create a Derivative Model from the Model through methods such as fine-tuning, downsizing by quantization, code editing, and parameter modification. However, the creation of a Derivative Model for purposes set forth in Paragraph 4 of the preceding Article or for any other commercial purposes is strictly prohibited.
(2) Upon creating any Derivative Models, the User must include and clearly display the prefix "PLaMo" in the names of these Derivative Models.
Article 5: Output
(1) The User may publicize Outputs, provided that it is clearly stated that they are the outputs generated by the Model or Derivative Models.
(2) The User is strictly prohibited from utilizing the Outputs for the purpose of developing, training, or enhancing any other large language models that are not the Model or Derivative Models.
Article 6: Other Usage Restrictions
In relation to the usage of the Model, Derivative Models, or Outputs (collectively defined as the "Models and Outputs"), the User is strictly prohibited from committing any of the listed acts:
(1) Violating any laws and regulations or disrupting public order and societal norms
(2) Infringing upon the rights or interests of PFN or any third party
(3) Tarnishing the reputation or credibility of PFN or any third party
(4) Inflicting financial damage upon PFN or any third party
(5) Making intimidations, racial discrimination, or defamatory remarks
(6) Inputting personal information as defined by Japanese law, specifically Paragraph 1 of Article 2 of the Act on the Protection of Personal Information (Act No. 57 of 2003), or sensitive personal information as similarly defined by this statute.
(7) Stalking, harassing, trolling, or doxxing other users
(8) Developing, endorsing, or using computer viruses, malicious software, automated software or bots, or harmful programs
(9) Engaging in any communication, action, or expression that can incite or encourage harmful actions such as suicide, self-abuse, violence, and drug use
(10) Communicating false information
(11) Circulating information implying the Outputs to be the official view and opinion of PFN
(12) Using the Models and Outputs in finance, education, employment, housing, insurance, legal, medical, or any other areas where such usage could have a legal or significant impact on any individual or business entity
(13) Relying on the Models and Outputs as the only source of information or as an alternative for expert advice
(14) Utilizing the Models and Outputs for vehicle navigation or for automated driving systems
(15) Engaging in, threatening to commit, participating in, or assisting in any criminal activities or any activities related thereto
(16) Engaging in money laundering or similar financial malpractices
(17) Providing direct or indirect benefits to anti-social forces
(18) Circulating obscene content or materials detrimental to the healthy development of young individuals
(19) Utilizing the Models and Outputs for political activities or activities of similar nature
(20) Acquiring the Model through methods other than the interface provided by PFN
(21) In addition to the aforementioned, any conduct deemed reasonably inappropriate as per PFN’s discretion
Article 7: Disclaimer of Warranty
THE MODEL AND OUTPUTS ARE PROVIDED ON AN "AS IS" BASIS. PFN MAKES NO GUARANTEES OR ASSURANCES OF ANY KIND IN RELATION TO THEM, INCLUDING BUT NOT LIMITED TO THEIR ACCURACY, AUTHENTICITY, MERCHANTABILITY, QUALITY, PERFORMANCE, APPLICABILITY FOR A PARTICULAR USE, OR NON-INFRINGEMENT OF ANY RIGHTS. IT FALLS ON THE USER TO DISCERN THE APPROPRIATENESS OF USING THE MODELS AND OUTPUTS, AND THE USER WILL ASSUME FULL RESPONSIBILITY FOR ALL CONSEQUENCES AS A RESULT OF THE USE OF THE MODELS AND OUTPUTS.
Article 8: Limitation of Liability
(1) PFN'S LIABILITY FOR ANY DAMAGE INCURRED BY THE USER, IN RELATION TO THIS AGREEMENT AND THE MODELS AND OUTPUTS, WHETHER ARISING FROM CONTRACT, TORT, PRODUCT LIABILITY OR ANY OTHER LEGAL CLAIM, WILL BE LIMITED TO DIRECT AND GENERAL DAMAGES ONLY (PFN WILL NOT BE HELD RESPONSIBLE FOR ANY LOSS OF PROFITS, SPECIAL, INDIRECT, OR ANY OTHER DAMAGES, WHETHER SUCH DAMAGES WERE FORESEEABLE OR UNFORESEEABLE.), AND THE MAXIMUM LIABILITY OF DAMAGES SHALL BE 500 YEN. THIS PROVISION, HOWEVER, DOES NOT APPLY IF PFN IS DETERMINED TO HAVE ACTED WITH DELIBERATE INTENT OR GROSS NEGLIGENCE.
(2) REGARDLESS OF THE PREVIOUS PARAGRAPH, SHOULD THE USER USE THE MODELS AND OUTPUTS FOR BUSINESS PURPOSES, PFN WILL NOT BEAR ANY LIABILITY TO THE USER FOR ANY DAMAGES OR OTHER LIABILITIES REGARDING THIS AGREEMENT AND THE MODELS AND OUTPUTS.
Article 9: User's Responsibility
(1) The User must ensure their acquisition and use of the Models and Outputs is in compliance with all relevant laws and regulations, including but not limited to those concerning import, export, and trade, in addition to the terms and conditions of this Agreement.
(2) The User shall compensate for any damages incurred by PFN as a result of the User's violation of this Agreement or use of the Models and Outputs.
(3) Should PFN be subject to any third-party claims for damages or any other liabilities resulting from the User's usage of the Models and Outputs, it is the User’s responsibility to absolve PFN from such claims and safeguard PFN against any potential liabilities.
Article 10: Ownership of Rights
(1) All rights pertaining to the Model shall belong to PFN or third parties who have licensed the Model to PFN.
(2) The User holds the rights to the modifications they make to the Model when creating Derivative Models, while PFN retains the rights in all remaining parts of the Derivative Models.
(3) The User holds all rights pertaining to the Outputs.
Article 11: Termination of Agreement
PFN reserves the right to terminate this Agreement at any given time and at its sole discretion.
Article 12: Duration of Agreement
(1) This Agreement will take effect when the User either agrees to its terms or accesses the Model, whichever occurs first, and will remain in effect until it is terminated.
(2) Upon the termination of the Agreement, regardless of the reasons, the User shall immediately cease all use of the Model and Derivative Models and delete all of them.
Article 13: Revision of Agreement
PFN may revise this Agreement (including the rules and regulations concerning the Models and Outputs; the same shall apply hereinafter in this Article). PFN shall announce any revisions to this Agreement, including the details of the changes and their effective date, in a prescribed manner by PFN, and prior to the implementation of the changes.
Article 14: Governing Law and Court of Jurisdiction
(1) This Agreement shall be governed by the laws of Japan.
(2) Any conflicts arising out of or in connection with this Agreement or the Models and Outputs shall be settled under the exclusive jurisdiction of the Tokyo District Court.
Log in or Sign Up to review the conditions and access this model content.
PLaMo-100B
Model Description
PLaMo-100B is a 100B model pre-trained on English and Japanese open datasets, developed by Preferred Elements, Inc. PLaMo-100B is released under both Commercial and Non-Commercial Licenses. Please check the LICENSE for non-commercial use, both Japanese version and English version of the license are available. For commercial use, please contact us via this form （Japanese Only）.
NOTE: This model has NOT been instruction-tuned for chat dialog or other downstream tasks. We provide instruction-tuned version of PLaMo-100B model via our API and solution packages. Please check our official PLaMo website (Japanese only) for details.
Usage
Requirements
- numpy
- sentencepiece
- torch
- transformers
Use a pipeline as a high-level helper
import transformers
pipeline = transformers.pipeline("text-generation", model="pfnet/plamo-100b", trust_remote_code=True)
print(pipeline("The future of artificial intelligence technology is ", max_new_tokens=32))
Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("pfnet/plamo-100b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("pfnet/plamo-100b", trust_remote_code=True)
text = "これからの人工知能技術は"
input_ids = tokenizer(text, return_tensors="pt").input_ids
generated_tokens = model.generate(
inputs=input_ids,
max_new_tokens=32,
do_sample=True,
top_k=50,
top_p=0.95,
temperature=1.0,
)[0]
generated_text = tokenizer.decode(generated_tokens)
print(generated_text)
Model Details
- Model size: 100B
- Trained tokens: 2T tokens (English: 1.3T tokens, Japanese: 0.7T tokens)
- Developed by: Preferred Elements, Inc
- Model type: Causal decoder-only
- Language(s): English, Japanese
- License: Commercial and Non-Commercial
Training Dataset
We trained PLaMo-100B in two phases, phase 1 with 1.5T tokens and phase 2 with 0.5T tokens. The percentage of datasets in each phase is shown in the following table.
| 1.5T (phase 1) | 0.5T (phase 2) | |
|---|---|---|
| RefinedWeb (English) | 42% | 17% |
| Other English Dataset | 28% | 33% |
| Proprietary CommonCrawl-JP | 18% | 46% |
| Other Japanese Dataset | 12% | 4% |
Tokenizer
PLaMo-100B uses sentencepiece tokenizer which is trained on a subset of the datasets for model pre-training.
Tech Blog
https://tech.preferred.jp/ja/blog/plamo-100b/
Bias, Risks, and Limitations
PLaMo-100B is a new technology that carries risks with use. Testing conducted to date has been in English and Japanese, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, PLaMo-100B’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of PLaMo-100B, developers should perform safety testing and tuning tailored to their specific applications of the model.
How to cite
@article{plamo100b,
author = {Preferred Elements, Inc. and Kenshin Abe and Kaizaburo Chubachi and Yasuhiro Fujita and Yuta Hirokawa and Kentaro Imajo and Toshiki Kataoka and Hiroyoshi Komatsu and Hiroaki Mikami and Tsuguo Mogami and Shogo Murai and Kosuke Nakago and Daisuke Nishino and Toru Ogawa and Daisuke Okanohara and Yoshihiko Ozaki and Shotaro Sano and Shuji Suzuki and Tianqi Xu and Toshihiko Yanase},
title = {PLaMo-100B: A Ground-Up Language Model Designed for Japanese Proficiency},
year = {2024},
url = {https://arxiv.org/abs/2410.07563},
journal = {arXiv}
}
Acknowledgement
This model is trained under the project, “Research and Development Project of the Enhanced Infrastructures for Post 5G Information and Communication System” (JPNP 20017), subsidized by the New Energy and Industrial Technology Development Organization (NEDO).
- Downloads last month
- 31