Update README & LICENSE files

LICENSE

@@ -0,0 +1,85 @@
+# Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
+
+This work is licensed under the **Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License**.
+
+To view a copy of this license, visit [https://creativecommons.org/licenses/by-nc-sa/4.0/](https://creativecommons.org/licenses/by-nc-sa/4.0/) or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+---
+
+## **Summary of Terms**
+You are free to:
+- **Share** — copy and redistribute the material in any medium or format.
+- **Adapt** — remix, transform, and build upon the material.
+
+**Under the following terms:**
+- **Attribution (BY):** You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
+- **Non-Commercial (NC):** You may not use the material for commercial purposes.
+- **ShareAlike (SA):** If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
+
+**No additional restrictions:** You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
+
+---
+
+## **Attribution Requirements**
+When redistributing or adapting this work, you must include the following attribution in a clear and visible manner:
+
+```
+This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).
+Original works:
+- Base Model: [https://huggingface.co/llm-jp/llm-jp-3-13b](https://huggingface.co/llm-jp/llm-jp-3-13b) (Apache License 2.0)
+- Datasets:
+  - [Aratako/HelpSteer2-Preferences-formatted](https://huggingface.co/datasets/Aratako/HelpSteer2-Preferences-formatted)
+  - [Aratako/Magpie-Tanuki-Instruction-Selected-Evolved-26.5k](https://huggingface.co/datasets/Aratako/Magpie-Tanuki-Instruction-Selected-Evolved-26.5k)
+  - [Aratako/Magpie-Tanuki-Qwen2.5-72B-Answered](https://huggingface.co/datasets/Aratako/Magpie-Tanuki-Qwen2.5-72B-Answered)
+  - [Aratako/Open-Platypus-Japanese-masked-formatted](https://huggingface.co/datasets/Aratako/Open-Platypus-Japanese-masked-formatted)
+  - [Aratako/Self-Instruct-Qwen2.5-72B-Instruct-60k](https://huggingface.co/datasets/Aratako/Self-Instruct-Qwen2.5-72B-Instruct-60k)
+  - [Aratako/Synthetic-JP-EN-Coding-Dataset-801k-50k](https://huggingface.co/datasets/Aratako/Synthetic-JP-EN-Coding-Dataset-801k-50k)
+  - [Aratako/aya-ja-evol-instruct-calm3-dpo-masked-formatted](https://huggingface.co/datasets/Aratako/aya-ja-evol-instruct-calm3-dpo-masked-formatted)
+  - [Aratako/iterative-dpo-data-for-ORPO-iter3](https://huggingface.co/datasets/Aratako/iterative-dpo-data-for-ORPO-iter3)
+  - [Aratako/iterative-dpo-data-for-SimPO-iter2](https://huggingface.co/datasets/Aratako/iterative-dpo-data-for-SimPO-iter2)
+  - [Aratako/magpie-qwen2.5-32b-reasoning-100k-formatted](https://huggingface.co/datasets/Aratako/magpie-qwen2.5-32b-reasoning-100k-formatted)
+  - [Aratako/magpie-reasoning-llama-nemotron-70b-100k-filtered](https://huggingface.co/datasets/Aratako/magpie-reasoning-llama-nemotron-70b-100k-filtered)
+  - [Aratako/magpie-ultra-v0.1-formatted](https://huggingface.co/datasets/Aratako/magpie-ultra-v0.1-formatted)
+  - [Aratako/orca-agentinstruct-1M-v1-selected](https://huggingface.co/datasets/Aratako/orca-agentinstruct-1M-v1-selected)
+  - [DeL-TaiseiOzaki/Tengentoppa-sft-qwen2.5-32b-reasoning-100k](https://huggingface.co/datasets/DeL-TaiseiOzaki/Tengentoppa-sft-qwen2.5-32b-reasoning-100k)
+  - [cl-nagoya/auto-wiki-qa](https://huggingface.co/datasets/cl-nagoya/auto-wiki-qa)
+  - [ichikara-instruction](https://liat-aip.sakura.ne.jp/wp/llm%E3%81%AE%E3%81%9F%E3%82%81%E3%81%AE%E6%97%A5%E6%9C%AC%E8%AA%9E%E3%82%A4%E3%83%B3%E3%82%B9%E3%83%88%E3%83%A9%E3%82%AF%E3%82%B7%E3%83%A7%E3%83%B3%E3%83%87%E3%83%BC%E3%82%BF%E4%BD%9C%E6%88%90/)
+  - [kanhatakeyama/ramdom-to-fixed-multiturn-Calm3](https://huggingface.co/datasets/kanhatakeyama/ramdom-to-fixed-multiturn-Calm3)
+  - [kanhatakeyama/wizardlm8x22b-logical-math-coding-sft_additional-ja](https://huggingface.co/datasets/kanhatakeyama/wizardlm8x22b-logical-math-coding-sft_additional-ja)
+  - [llm-jp/magpie-sft-v1.0](https://huggingface.co/datasets/llm-jp/magpie-sft-v1.0)
+  - [saillab/alpaca-japanese-cleaned](https://huggingface.co/datasets/saillab/alpaca-japanese-cleaned)
+  - [tokutsu/japanese-tasks1000](https://huggingface.co/datasets/tokutsu/japanese-tasks1000)
+- Used Models for scoring:
+  - [Qwen/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) (Built with Qwen)
+  - [meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) (Built with Llama)
+- Used Models for datasets (including third parties' datasets):
+  - [AIDC-AI/Marco-o1](https://huggingface.co/AIDC-AI/Marco-o1)
+  - [Aratako/Llama-Gemma-2-27b-CPO_SimPO-iter1](https://huggingface.co/Aratako/Llama-Gemma-2-27b-CPO_SimPO-iter1)
+  - [Aratako/Llama-Gemma-2-27b-CPO_SimPO-iter2](https://huggingface.co/Aratako/Llama-Gemma-2-27b-CPO_SimPO-iter2)
+  - Google Cloud Translation
+  - [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) (Built with Qwen)
+  - [Qwen/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) (Built with Qwen)
+  - [Qwen/Qwen2.5-72B-Instruct-GPTQ-Int8](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct-GPTQ-Int8) (Built with Qwen)
+  - [WizardLM 8x22b](https://github.com/nlpxucan/WizardLM)
+  - [cl-nagoya/ruri-large](https://huggingface.co/cl-nagoya/ruri-large)
+  - [cyberagent/calm3-22b-chat](https://huggingface.co/cyberagent/calm3-22b-chat)
+  - [meta-llama/Llama-3.1-405B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct) (Built with Llama)
+  - [meta-llama/Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct) (Built with Llama)
+  - [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) (Built with Llama)
+  - [meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) (Built with Llama)
+  - [meta-llama/Llama-Guard-3-8B](https://huggingface.co/meta-llama/Llama-Guard-3-8B)
+  - [microsoft/Phi-3-medium-4k-instruct](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct)
+  - [nvidia/Nemotron-4-340B-Instruct](https://huggingface.co/nvidia/Nemotron-4-340B-Instruct)
+  - [team-hatakeyama-phase2/Tanuki-8x8B-dpo-v1.0-GPTQ-8bit](https://huggingface.co/team-hatakeyama-phase2/Tanuki-8x8B-dpo-v1.0-GPTQ-8bit)
+  - [team-hatakeyama-phase2/tanuki-8B-exp007](https://huggingface.co/team-hatakeyama-phase2/tanuki-8B-exp007)
+  - [tokyotech-llm/Swallow-MX-8x7b-NVE-v0.1](https://huggingface.co/tokyotech-llm/Swallow-MX-8x7b-NVE-v0.1)
+  - [weblab-GENIAC/Tanuki-8B-dpo-v1.0](https://huggingface.co/weblab-GENIAC/Tanuki-8B-dpo-v1.0)
+This work:
+- Model: CC BY-NC-SA 4.0
+- Creator: tokutsu
+```
+
+---
+
+**Disclaimer:**
+The materials are 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, or non-infringement.

LICENSES/LICENSE_LLAMA_3.1.txt

@@ -0,0 +1,114 @@
+LLAMA 3.1 COMMUNITY LICENSE AGREEMENT
+Llama 3.1 Version Release Date: July 23, 2024
+
+“Agreement” means the terms and conditions for use, reproduction, distribution and modification of the
+Llama Materials set forth herein.
+
+“Documentation” means the specifications, manuals and documentation accompanying Llama 3.1
+distributed by Meta at https://llama.meta.com/doc/overview.
+
+“Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into
+this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or
+regulations to provide legal consent and that has legal authority to bind your employer or such other
+person or entity if you are entering in this Agreement on their behalf.
+
+“Llama 3.1” means the foundational large language models and software and algorithms, including
+machine-learning model code, trained model weights, inference-enabling code, training-enabling code,
+fine-tuning enabling code and other elements of the foregoing distributed by Meta at
+https://llama.meta.com/llama-downloads.
+
+“Llama Materials” means, collectively, Meta’s proprietary Llama 3.1 and Documentation (and any
+portion thereof) made available under this Agreement.
+
+“Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your
+principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located
+outside of the EEA or Switzerland).
+
+By clicking “I Accept” below or by using or distributing any portion or element of the Llama Materials,
+you agree to be bound by this Agreement.
+
+1. License Rights and Redistribution.
+
+  a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free
+limited license under Meta’s intellectual property or other rights owned by Meta embodied in the Llama
+Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the
+Llama Materials.
+
+  b. Redistribution and Use.
+
+      i. If you distribute or make available the Llama Materials (or any derivative works
+thereof), or a product or service (including another AI model) that contains any of them, you shall (A)
+provide a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with
+Llama” on a related website, user interface, blogpost, about page, or product documentation. If you use
+the Llama Materials or any outputs or results of the Llama Materials to create, train, fine tune, or
+otherwise improve an AI model, which is distributed or made available, you shall also include “Llama” at
+the beginning of any such AI model name.
+
+      ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part 
+of an integrated end user product, then Section 2 of this Agreement will not apply to you.
+
+      iii. You must retain in all copies of the Llama Materials that you distribute the following
+attribution notice within a “Notice” text file distributed as a part of such copies: “Llama 3.1 is
+licensed under the Llama 3.1 Community License, Copyright © Meta Platforms, Inc. All Rights
+Reserved.”
+
+      iv. Your use of the Llama Materials must comply with applicable laws and regulations
+(including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama
+Materials (available at https://llama.meta.com/llama3_1/use-policy), which is hereby incorporated by
+reference into this Agreement.
+
+2. Additional Commercial Terms. If, on the Llama 3.1 version release date, the monthly active users
+of the products or services made available by or for Licensee, or Licensee’s affiliates, is greater than 700
+million monthly active users in the preceding calendar month, you must request a license from Meta,
+which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the
+rights under this Agreement unless or until Meta otherwise expressly grants you such rights.
+
+3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY
+OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF
+ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED,
+INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT,
+MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR
+DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND
+ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND
+RESULTS.
+
+4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF
+LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING
+OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL,
+INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED
+OF THE POSSIBILITY OF ANY OF THE FOREGOING.
+
+5. Intellectual Property.
+
+  a. No trademark licenses are granted under this Agreement, and in connection with the Llama
+Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other
+or any of its affiliates, except as required for reasonable and customary use in describing and
+redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to
+use “Llama” (the “Mark”) solely as required to comply with the last sentence of Section 1.b.i. You will
+comply with Meta’s brand guidelines (currently accessible at
+https://about.meta.com/brand/resources/meta/company-brand/ ). All goodwill arising out of your use
+of the Mark will inure to the benefit of Meta.
+
+  b. Subject to Meta’s ownership of Llama Materials and derivatives made by or for Meta, with
+respect to any derivative works and modifications of the Llama Materials that are made by you, as
+between you and Meta, you are and will be the owner of such derivative works and modifications.
+
+  c. If you institute litigation or other proceedings against Meta or any entity (including a
+cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 3.1 outputs or
+results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other
+rights owned or licensable by you, then any licenses granted to you under this Agreement shall
+terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold
+harmless Meta from and against any claim by any third party arising out of or related to your use or
+distribution of the Llama Materials.
+
+6. Term and Termination. The term of this Agreement will commence upon your acceptance of this
+Agreement or access to the Llama Materials and will continue in full force and effect until terminated in
+accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in
+breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete
+and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this
+Agreement.
+
+7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of
+the State of California without regard to choice of law principles, and the UN Convention on Contracts
+for the International Sale of Goods does not apply to this Agreement. The courts of California shall have
+exclusive jurisdiction of any dispute arising out of this Agreement.

LICENSES/LICENSE_LLAMA_3.3.txt

@@ -0,0 +1,49 @@
+**LLAMA 3.3 COMMUNITY LICENSE AGREEMENT**
+
+ Llama 3.3 Version Release Date: December 6, 2024
+
+“**Agreement**” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein.
+
+“**Documentation**” means the specifications, manuals and documentation accompanying Llama 3.3 distributed by Meta at [https://www.llama.com/docs/overview](https://llama.com/docs/overview).
+
+“**Licensee**” or “**you**” means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf.
+
+“**Llama 3.3**” means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at [https://www.llama.com/llama-downloads](https://www.llama.com/llama-downloads).
+
+“**Llama Materials**” means, collectively, Meta’s proprietary Llama 3.3 and Documentation (and any portion thereof) made available under this Agreement.
+
+“**Meta**” or “**we**” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland). 
+
+By clicking “I Accept” below or by using or distributing any portion or element of the Llama Materials, you agree to be bound by this Agreement.
+
+1\. **License Rights and Redistribution**.
+
+a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta’s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials.  
+
+b. Redistribution and Use.  
+
+i. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service (including another AI model) that contains any of them, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with Llama” on a related website, user interface, blogpost, about page, or product documentation. If you use the Llama Materials or any outputs or results of the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include “Llama” at the beginning of any such AI model name.
+
+ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you. 
+
+iii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a “Notice” text file distributed as a part of such copies: “Llama 3.3 is licensed under the Llama 3.3 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.”
+
+iv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at [https://www.llama.com/llama3\_3/use-policy](https://www.llama.com/llama3_3/use-policy)), which is hereby incorporated by reference into this Agreement.  
+    
+2\. **Additional Commercial Terms**. If, on the Llama 3.3 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee’s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights.
+
+3**. Disclaimer of Warranty**. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.
+
+4\. **Limitation of Liability**. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.
+
+5\. **Intellectual Property**.
+
+a. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to use “Llama” (the “Mark”) solely as required to comply with the last sentence of Section 1.b.i. You will comply with Meta’s brand guidelines (currently accessible at [https://about.meta.com/brand/resources/meta/company-brand/](https://about.meta.com/brand/resources/meta/company-brand/)[)](https://en.facebookbrand.com/). All goodwill arising out of your use of the Mark will inure to the benefit of Meta.
+
+b. Subject to Meta’s ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications.
+
+c. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 3.3 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials.
+
+6\. **Term and Termination**. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement. 
+
+7\. **Governing Law and Jurisdiction**. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement.   

LICENSES/LICENSE_LLM_JP_3.txt

@@ -0,0 +1,204 @@
+This license applies to the base model: llm-jp/llm-jp-3-13b (Apache License 2.0)
+--------------------------------------------------------------------------------
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
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+
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+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
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+      of this License, Derivative Works shall not include works that remain
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+      "Contribution" shall mean any work of authorship, including
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README.md

@@ -1,67 +1,274 @@
 ---
-base_model: tokutsu/llm-jp-3-13b_alpaca-japanese_orpo20250302
-library_name: transformers
-model_name: llm-jp-3-13b_auto-wiki-qa200k_orpo20250310
+license: cc-by-nc-sa-4.0
 tags:
-- generated_from_trainer
-- axolotl
-- trl
-- orpo
-licence: license
+  - japanese
+  - instruction-tuning
+  - preference-optimization
+  - quantized
+  - causal-lm
+  - axolotl
+  - orpo
+  - trl
+  - exllamav2
+  - exl2
+  - 8-bit precision
+  - text-generation-inference
+  - Inference Endpoints
+  - ELYZA-tasks-100
+  - Japanese MT-Bench
+  - japanese-answer
+language:
+  - ja
+model_name: japanese-answer-13b-8bit
+base_model: llm-jp/llm-jp-3-13b
+datasets:
+  - Aratako/HelpSteer2-Preferences-formatted
+  - Aratako/Magpie-Tanuki-Instruction-Selected-Evolved-26.5k
+  - Aratako/Magpie-Tanuki-Qwen2.5-72B-Answered
+  - Aratako/Open-Platypus-Japanese-masked-formatted
+  - Aratako/Self-Instruct-Qwen2.5-72B-Instruct-60k
+  - Aratako/Synthetic-JP-EN-Coding-Dataset-801k-50k
+  - Aratako/aya-ja-evol-instruct-calm3-dpo-masked-formatted
+  - Aratako/iterative-dpo-data-for-ORPO-iter3
+  - Aratako/iterative-dpo-data-for-SimPO-iter2
+  - Aratako/magpie-qwen2.5-32b-reasoning-100k-formatted
+  - Aratako/magpie-reasoning-llama-nemotron-70b-100k-filtered
+  - Aratako/magpie-ultra-v0.1-formatted
+  - Aratako/orca-agentinstruct-1M-v1-selected
+  - DeL-TaiseiOzaki/Tengentoppa-sft-qwen2.5-32b-reasoning-100k
+  - cl-nagoya/auto-wiki-qa
+  - ichikara-instruction
+  - kanhatakeyama/ramdom-to-fixed-multiturn-Calm3
+  - kanhatakeyama/wizardlm8x22b-logical-math-coding-sft_additional-ja
+  - llm-jp/magpie-sft-v1.0
+  - saillab/alpaca-japanese-cleaned
+  - tokutsu/japanese-tasks1000
+quantized_by: exllamav2
+metrics:
+  - elyza/ELYZA-tasks-100
+  - Stability-AI/japanese_mt_bench
 ---
 
-# Model Card for llm-jp-3-13b_auto-wiki-qa200k_orpo20250310
+# Japanese-Answer 13B 8bit
 
-This model is a fine-tuned version of [tokutsu/llm-jp-3-13b_alpaca-japanese_orpo20250302](https://huggingface.co/tokutsu/llm-jp-3-13b_alpaca-japanese_orpo20250302).
-It has been trained using [TRL](https://github.com/huggingface/trl).
+Instruction-tuned and Preference-optimized Japanese LLM based on **LLM-JP-3 13B** pre-trained model, quantized to **8bit (EXL2)** using **ExLlamaV2**. Trained on a mix of Japanese instruction and preference datasets.
 
-## Quick start
+## Benchmarks
+### ELYZA-tasks-100
 
-```python
-from transformers import pipeline
+| Model                                    | Avg. Score (1–5) |
+|------------------------------------------|------------------|
+| This Model (13B 8bit, SFT+PO)            | **3.69**         |
+
+- Score is based on automatic evaluation with GPT-3.5-Turbo.
+- Task set is based on [ELYZA-tasks-100](https://huggingface.co/datasets/elyza/ELYZA-tasks-100).
+
+### Japanese MT-Bench
+
+| coding | extraction | humanities | math | reasoning | roleplay | stem | writing | Avg. (1-10) |
+|--|--|--|--|--|--|--|--|--|
+| 3.20 | 7.05 | 9.60 | 3.00 | 6.00 | 6.90 | 8.65 | 7.15 | **6.44** |
+
+- **Average score**: **6.44 / 10**
+- Evaluated using **GPT-4** as the judge (single-answer grading mode).
+- Based on the official [Japanese MT-Bench](https://github.com/Stability-AI/FastChat).
+
+## What's special
+
+- Based on [llm-jp/llm-jp-3-13b](https://huggingface.co/llm-jp/llm-jp-3-13b) (pre-trained model).
+- SFT + Preference Optimization (PO) applied.
+- Quantized using [ExLlamaV2](https://github.com/turboderp/exllamav2) to **EXL2 8.0bpw**.
+- Focused on practical improvements in Japanese instruction-following and QA.
+
+## Usage
+
+This model is compatible with ExLlamaV2 for fast inference on quantized models (EXL2 format).
+Below is a minimal guide to install, download, and run inference using ExLlamaV2.
+
+### 0. Hardware Requirements
+
+This model is quantized in EXL2 format and optimized for ExLlamaV2 to enable fast, memory-efficient inference.
+However, it requires an NVIDIA GPU with **Ampere architecture or newer**, due to reliance on FlashAttention and low-bit optimizations. Supported GPUs include:
+
+- Ampere: A100, A10, RTX 30 series
+- Ada Lovelace: L4
+- Hopper: H100
 
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="tokutsu/llm-jp-3-13b_auto-wiki-qa200k_orpo20250310", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
+GPUs like **T4** or **V100** are **not supported**, and attempting to use this model on such GPUs may result in runtime errors due to FlashAttention incompatibility.
+
+### 1. Installation
+
+```bash
+git clone -b v0.2.6 https://github.com/turboderp/exllamav2.git
+git clone https://huggingface.co/spaces/tokutsu/exllamav2_patch
+
+cd exllamav2
+patch -p1 < ../exllamav2_patch/hf.py.patch  # to support unigram tokenizer
+pip install -r requirements.txt
+pip install .
 ```
 
-## Training procedure
 
-[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/tokutsu-ai-next-inc-/axolotl/runs/ngvgqk5p)
+### 2. Download the model
 
-This model was trained with ORPO, a method introduced in [ORPO: Monolithic Preference Optimization without Reference Model](https://huggingface.co/papers/2403.07691).
+```bash
+pip install huggingface_hub
 
-### Framework versions
+huggingface-cli download tokutsu/japanese-answer-13b-8bit \
+  --local-dir ./model --include "*.safetensors" "*.json" "*.txt"
+```
+
+### 3. Inference
 
-- TRL: 0.12.1
-- Transformers: 4.47.1
-- Pytorch: 2.5.1
-- Datasets: 3.1.0
-- Tokenizers: 0.21.0
+```python
+from exllamav2 import ExLlamaV2, ExLlamaV2Cache, ExLlamaV2Config, ExLlamaV2Tokenizer
+from exllamav2.generator import ExLlamaV2DynamicGenerator, ExLlamaV2Sampler
 
-## Citations
+# Load model
+model_path = "./model"
+config = ExLlamaV2Config(model_path)
+model = ExLlamaV2(config)
+tokenizer = ExLlamaV2Tokenizer(config)
+cache = ExLlamaV2Cache(model, lazy=True)
+model.load_autosplit(cache)
 
-Cite ORPO as:
+# Setup generator
+generator = ExLlamaV2DynamicGenerator(model=model, tokenizer=tokenizer, cache=cache)
+gen_settings = ExLlamaV2Sampler.Settings.greedy()
 
-```bibtex
-@article{hong2024orpo,
-    title        = {{ORPO: Monolithic Preference Optimization without Reference Model}},
-    author       = {Jiwoo Hong and Noah Lee and James Thorne},
-    year         = 2024,
-    eprint       = {arXiv:2403.07691}
-}
+# Prompt
+prompt = """次の指示に答えて下さい。
+
+### 指示
+仕事の熱意を取り戻すためのアイデアを5つ挙げてください。
+
+### 解答
+"""
+
+# Generate
+output = generator.generate(
+    prompt=[prompt],
+    max_new_tokens=1536,
+    settings=gen_settings,
+    stop_conditions=[tokenizer.eos_token_id],
+    encode_special_tokens=True,
+)
+print(output[0])
 ```
 
-Cite TRL as:
-    
-```bibtex
-@misc{vonwerra2022trl,
-	title        = {{TRL: Transformer Reinforcement Learning}},
-	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
-	year         = 2020,
-	journal      = {GitHub repository},
-	publisher    = {GitHub},
-	howpublished = {\url{https://github.com/huggingface/trl}}
-}
-```
+## Example Output
+
+Here is an example of what the output would look like (using a task from the **ELYZA-tasks-100** dataset):
+
+```plaintext
+次の指示に答えて下さい。
+
+### 指示
+仕事の熱意を取り戻すためのアイデアを5つ挙げてください。
+
+### 解答
+1. **目標設定の見直し**: 現在の業務目標が自分にとって意義深いものであるか確認し、必要に応じて再定義します。例えば、短期的な成果だけでなく、長期的なキャリアパスを見据えた目標設定を行うことで、モチベーションが向上します。
+2. **スキルアップの機会を探す**: 新しい技術や知識を習得することで、自己成長を感じることができます。オンラインコースやワークショップに参加したり、同僚との勉強会を開催したりすることで、刺激を受けることができます。
+3. **ワークライフバランスの改善**: 過度な労働時間を見直し、プライベート時間を確保することで、心身ともにリフレッシュできます。例えば、週に一度の完全オフデーを作ることや、趣味活動を再開することが有効です。
+4. **チームビルディング活動の実施**: 同僚との絆を深めることで、職場環境がより楽しくなります。定期的なランチミーティングやスポーツイベントなどを企画することで、コミュニケーションが活発になり、互いに支え合う関係が築けます。
+5. **外部からのフィードバックを求める**: 第三者からの客観的な意見を取り入れることで、自己評価を見直すことができます。メンターシッププログラムに参加したり、外部コンサルタントからのアドバイスを受けたりするのも一つの方法です。
+```
+
+## Training Details
+
+### Datasets
+
+#### (1) SFT
+
+| Dataset name | (Derived from) |
+|--|--|
+| [Aratako/Magpie-Tanuki-Qwen2.5-72B-Answered](https://huggingface.co/datasets/Aratako/Magpie-Tanuki-Qwen2.5-72B-Answered) | |
+| [Aratako/Open-Platypus-Japanese-masked-formatted](https://huggingface.co/datasets/Aratako/Open-Platypus-Japanese-masked-formatted) | [weblab-GENIAC/Open-Platypus-Japanese-masked](https://huggingface.co/datasets/weblab-GENIAC/Open-Platypus-Japanese-masked), [garage-bAInd/Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) |
+| [Aratako/Synthetic-JP-EN-Coding-Dataset-801k-50k](https://huggingface.co/datasets/Aratako/Synthetic-JP-EN-Coding-Dataset-801k-50k) | [Aratako/Synthetic-JP-EN-Coding-Dataset-801k](https://huggingface.co/datasets/Aratako/Synthetic-JP-EN-Coding-Dataset-801k), [Aratako/Synthetic-JP-EN-Coding-Dataset-Magpie-69k](https://huggingface.co/datasets/Aratako/Synthetic-JP-EN-Coding-Dataset-Magpie-69k) |
+| [Aratako/magpie-qwen2.5-32b-reasoning-100k-formatted](https://huggingface.co/datasets/Aratako/magpie-qwen2.5-32b-reasoning-100k-formatted) | [DeL-TaiseiOzaki/Tengentoppa-sft-qwen2.5-32b-reasoning-100k](https://huggingface.co/datasets/DeL-TaiseiOzaki/Tengentoppa-sft-qwen2.5-32b-reasoning-100k) |
+| [Aratako/magpie-reasoning-llama-nemotron-70b-100k-filtered](https://huggingface.co/datasets/Aratako/magpie-reasoning-llama-nemotron-70b-100k-filtered) | [DeL-TaiseiOzaki/Tengentoppa-sft-reasoning-llama-nemotron-70b-100k](https://huggingface.co/datasets/DeL-TaiseiOzaki/Tengentoppa-sft-reasoning-llama-nemotron-70b-100k) |
+| [Aratako/magpie-ultra-v0.1-formatted](https://huggingface.co/datasets/Aratako/magpie-ultra-v0.1-formatted) | [argilla/magpie-ultra-v0.1](https://huggingface.co/datasets/argilla/magpie-ultra-v0.1) |
+| [Aratako/orca-agentinstruct-1M-v1-selected](https://huggingface.co/datasets/Aratako/orca-agentinstruct-1M-v1-selected) | [microsoft/orca-agentinstruct-1M-v1](https://huggingface.co/datasets/microsoft/orca-agentinstruct-1M-v1) |
+| [ichikara-instruction](https://liat-aip.sakura.ne.jp/wp/llm%E3%81%AE%E3%81%9F%E3%82%81%E3%81%AE%E6%97%A5%E6%9C%AC%E8%AA%9E%E3%82%A4%E3%83%B3%E3%82%B9%E3%83%88%E3%83%A9%E3%82%AF%E3%82%B7%E3%83%A7%E3%83%B3%E3%83%87%E3%83%BC%E3%82%BF%E4%BD%9C%E6%88%90/) | |
+| [kanhatakeyama/ramdom-to-fixed-multiturn-Calm3](https://huggingface.co/datasets/kanhatakeyama/ramdom-to-fixed-multiturn-Calm3) | |
+| [kanhatakeyama/wizardlm8x22b-logical-math-coding-sft_additional-ja](https://huggingface.co/datasets/kanhatakeyama/wizardlm8x22b-logical-math-coding-sft_additional-ja) | |
+| tokutsu/japanese-tasks1000 (currently not planned for release) | [elyza/ELYZA-tasks-100](https://huggingface.co/datasets/elyza/ELYZA-tasks-100) |
+
+#### (2) ORPO
+
+| Dataset name | (Derived from) |
+|--|--|
+| [Aratako/HelpSteer2-Preferences-formatted](https://huggingface.co/datasets/Aratako/HelpSteer2-Preferences-formatted) | [nvidia/HelpSteer2](https://huggingface.co/datasets/nvidia/HelpSteer2) |
+| [Aratako/Magpie-Tanuki-Instruction-Selected-Evolved-26.5k](https://huggingface.co/datasets/Aratako/Magpie-Tanuki-Instruction-Selected-Evolved-26.5k) | |
+| [Aratako/Self-Instruct-Qwen2.5-72B-Instruct-60k](https://huggingface.co/datasets/Aratako/Self-Instruct-Qwen2.5-72B-Instruct-60k) | |
+| [Aratako/aya-ja-evol-instruct-calm3-dpo-masked-formatted](https://huggingface.co/datasets/Aratako/aya-ja-evol-instruct-calm3-dpo-masked-formatted) | [weblab-GENIAC/aya-ja-evol-instruct-calm3-dpo-masked](https://huggingface.co/datasets/weblab-GENIAC/aya-ja-evol-instruct-calm3-dpo-masked) |
+| [Aratako/iterative-dpo-data-for-ORPO-iter3](https://huggingface.co/datasets/Aratako/iterative-dpo-data-for-ORPO-iter3) | |
+| [Aratako/iterative-dpo-data-for-SimPO-iter2](https://huggingface.co/datasets/Aratako/iterative-dpo-data-for-SimPO-iter2) | |
+| [DeL-TaiseiOzaki/Tengentoppa-sft-qwen2.5-32b-reasoning-100k](https://huggingface.co/datasets/DeL-TaiseiOzaki/Tengentoppa-sft-qwen2.5-32b-reasoning-100k) | |
+| [llm-jp/magpie-sft-v1.0](https://huggingface.co/datasets/llm-jp/magpie-sft-v1.0) | |
+| [saillab/alpaca-japanese-cleaned](https://huggingface.co/datasets/saillab/alpaca-japanese-cleaned) | [Alpaca-52K](https://github.com/tatsu-lab/stanford_alpaca) |
+
+#### (3) SimPO/CPO
+
+| Dataset name | (Derived from) |
+|--|--|
+| [Aratako/HelpSteer2-Preferences-formatted](https://huggingface.co/datasets/Aratako/HelpSteer2-Preferences-formatted) | [nvidia/HelpSteer2](https://huggingface.co/datasets/nvidia/HelpSteer2) |
+| [Aratako/Magpie-Tanuki-Instruction-Selected-Evolved-26.5k](https://huggingface.co/datasets/Aratako/Magpie-Tanuki-Instruction-Selected-Evolved-26.5k) | |
+| [Aratako/aya-ja-evol-instruct-calm3-dpo-masked-formatted](https://huggingface.co/datasets/Aratako/aya-ja-evol-instruct-calm3-dpo-masked-formatted) | [weblab-GENIAC/aya-ja-evol-instruct-calm3-dpo-masked](https://huggingface.co/datasets/weblab-GENIAC/aya-ja-evol-instruct-calm3-dpo-masked) |
+| [Aratako/iterative-dpo-data-for-SimPO-iter2](https://huggingface.co/datasets/Aratako/iterative-dpo-data-for-SimPO-iter2) | |
+| [cl-nagoya/auto-wiki-qa](https://huggingface.co/datasets/cl-nagoya/auto-wiki-qa) | [hpprc/jawiki](https://huggingface.co/datasets/hpprc/jawiki) , [Wikipedia](https://dumps.wikimedia.org/) |
+| [llm-jp/magpie-sft-v1.0](https://huggingface.co/datasets/llm-jp/magpie-sft-v1.0) | |
+
+### Models
+
+#### (1) Used for preference generation:
+
+| Model name |
+|--|
+| [Qwen/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) (Built with Qwen) |
+| [meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) (Built with Llama) |
+
+#### (2) Used in the datasets above:
+
+| Model name |
+|--|
+| [AIDC-AI/Marco-o1](https://huggingface.co/AIDC-AI/Marco-o1) |
+| [Aratako/Llama-Gemma-2-27b-CPO_SimPO-iter1](https://huggingface.co/Aratako/Llama-Gemma-2-27b-CPO_SimPO-iter1) |
+| [Aratako/Llama-Gemma-2-27b-CPO_SimPO-iter2](https://huggingface.co/Aratako/Llama-Gemma-2-27b-CPO_SimPO-iter2) |
+| Google Cloud Translation |
+| [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) (Built with Qwen) |
+| [Qwen/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) (Built with Qwen) |
+| [Qwen/Qwen2.5-72B-Instruct-GPTQ-Int8](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct-GPTQ-Int8) (Built with Qwen) |
+| [WizardLM 8x22b](https://github.com/nlpxucan/WizardLM) |
+| [cl-nagoya/ruri-large](https://huggingface.co/cl-nagoya/ruri-large) |
+| [cyberagent/calm3-22b-chat](https://huggingface.co/cyberagent/calm3-22b-chat) |
+| [meta-llama/Llama-3.1-405B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct) (Built with Llama) |
+| [meta-llama/Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct) (Built with Llama) |
+| [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) (Built with Llama) |
+| [meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) (Built with Llama) |
+| [meta-llama/Llama-Guard-3-8B](https://huggingface.co/meta-llama/Llama-Guard-3-8B) (Built with Llama) |
+| [microsoft/Phi-3-medium-4k-instruct](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct) |
+| [mistralai/Mixtral-8x22B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1) |
+| [nvidia/Nemotron-4-340B-Instruct](https://huggingface.co/nvidia/Nemotron-4-340B-Instruct) |
+| [team-hatakeyama-phase2/Tanuki-8x8B-dpo-v1.0-GPTQ-8bit](https://huggingface.co/team-hatakeyama-phase2/Tanuki-8x8B-dpo-v1.0-GPTQ-8bit) |
+| [team-hatakeyama-phase2/tanuki-8B-exp007](https://huggingface.co/team-hatakeyama-phase2/tanuki-8B-exp007) |
+| [tokyotech-llm/Swallow-MX-8x7b-NVE-v0.1](https://huggingface.co/tokyotech-llm/Swallow-MX-8x7b-NVE-v0.1) |
+| [weblab-GENIAC/Tanuki-8B-dpo-v1.0](https://huggingface.co/weblab-GENIAC/Tanuki-8B-dpo-v1.0) |
+
+### Libraries
+
+| Method | Library names |
+|--|--|
+| SFT | Axolotl, TRL, Unsloth |
+| ORPO, SimPO/CPO | Axolotl |
+| Quantization | ExLlamaV2 (EXL2) |
+
+## License
+
+- CC BY-NC-SA 4.0
+  - This model's license is described in the root `LICENSE` file.
+  - For third-party dependencies, please refer to the `LICENSES/` directory.
+
+## Acknowledgements
+
+- Special thanks to all developers and researchers whose prior projects made this work possible.