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add training details of Sarashina2-8x70B (#401)
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kaisugi authored Dec 1, 2024
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2 changes: 1 addition & 1 deletion README.md
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| | アーキテクチャ | 入出力で扱える<br>トークン数 | 学習テキスト | 開発元 | ライセンス / 利用規約 |
|:---|:---:|:---:|:---:|:---:|:---:|
| [Sarashina2-8x70B](https://www.sbintuitions.co.jp/news/press/20241108_01/) | Mixtral<br>([8x70b (**465b**)](https://huggingface.co/sbintuitions/sarashina2-8x70b)) | 8,192 | 不明 | SB Intuitions | Sarashina Model NonCommercial License |
| [Sarashina2-8x70B](https://www.sbintuitions.co.jp/news/press/20241108_01/) | Mixtral<br>([8x70b (**465b**)](https://huggingface.co/sbintuitions/sarashina2-8x70b)) | 8,192 | Sarashina2 (70B) に対して Sparse Upcycling で学習 | SB Intuitions | Sarashina Model NonCommercial License |
| [LLM-jp-3 172B beta2](https://llmc.nii.ac.jp/topics/llm-jp-3-172b-beta2/) | Llama<br>([**172b**-beta2](https://huggingface.co/llm-jp/llm-jp-3-172b-beta2), [**172b**-beta2-instruct2](https://huggingface.co/llm-jp/llm-jp-3-172b-beta2-instruct2)) | 4,096 | 事前学習: [llm-jp-corpus-v3](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)の一部<br>(計 **1.4T** トークン)<br>Instruction Tuning: [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/), [answer-carefully](https://liat-aip.sakura.ne.jp/wp/answercarefully-dataset/), [magpie-sft-v1.0](https://huggingface.co/datasets/llm-jp/magpie-sft-v1.0), Daring-Anteater, FLAN, ichikara-instruction-format, AutoMultiTurnByCalm3-22B, ramdom-to-fixed-multiturn-Calm3, wizardlm8x22b-logical-math-coding-sft-ja, wizardlm8x22b-logical-math-coding-sft_additional-ja, Synthetic-JP-EN-Coding-Dataset-567k | 大規模言語モデル研究開発センター (LLMC) | LLM-jp-3 172B beta2 Terms of Use |
| [LLM-jp-3 172B beta1](https://www.nii.ac.jp/news/release/2024/0917.html) | Llama<br>([**172b**-beta1](https://huggingface.co/llm-jp/llm-jp-3-172b-beta1), [**172b**-beta1-instruct](https://huggingface.co/llm-jp/llm-jp-3-172b-beta1-instruct)) | 4,096 | 事前学習: [llm-jp-corpus-v3](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)の一部<br>(計 **0.7T** トークン)<br>Instruction Tuning: [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/), [answer-carefully](https://liat-aip.sakura.ne.jp/wp/answercarefully-dataset/), Dolly Dataset, OASST1, OASST2, Aya Dataset, ichikara-instruction-format, Daring-Anteater, FLAN | 大規模言語モデル研究開発センター (LLMC) | LLM-jp-3 172B beta1 Terms of Use |
| [LLM-jp-3 172B alpha](https://llmc.nii.ac.jp/topics/llm-jp-3-172b-alpha1-alpha2/) | Llama<br>([**172b**-alpha1](https://huggingface.co/llm-jp/llm-jp-3-172b-alpha1), [**172b**-alpha1-instruct](https://huggingface.co/llm-jp/llm-jp-3-172b-alpha1-instruct), [**172b**-alpha2](https://huggingface.co/llm-jp/llm-jp-3-172b-alpha2), [**172b**-alpha2-instruct](https://huggingface.co/llm-jp/llm-jp-3-172b-alpha2-instruct)) | 4,096 | 事前学習: [llm-jp-corpus-v3](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)の一部<br>(alpha1: 計 **0.7T** トークン, alpha2: 計 **1.4T** トークン)<br>Instruction Tuning: [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/), [answer-carefully](https://liat-aip.sakura.ne.jp/wp/answercarefully-dataset/), Dolly Dataset, OASST1, OASST2, Aya Dataset, ichikara-instruction-format, Daring-Anteater, FLAN | 大規模言語モデル研究開発センター (LLMC) | Apache 2.0 |
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| | Architecture | Max Context Length | Training Data | Developer | License / Terms of Use |
|:---|:---:|:---:|:---:|:---:|:---:|
| [Sarashina2-8x70B](https://www.sbintuitions.co.jp/news/press/20241108_01/) | Mixtral<br>([8x70b (**465b**)](https://huggingface.co/sbintuitions/sarashina2-8x70b)) | 8,192 | undisclosed | SB Intuitions | Sarashina Model NonCommercial License |
| [Sarashina2-8x70B](https://www.sbintuitions.co.jp/news/press/20241108_01/) | Mixtral<br>([8x70b (**465b**)](https://huggingface.co/sbintuitions/sarashina2-8x70b)) | 8,192 | Sparse Upcycling on Sarashina2 (70B) | SB Intuitions | Sarashina Model NonCommercial License |
| [LLM-jp-3 172B beta2](https://llmc.nii.ac.jp/en/topics/llm-jp-3-172b-beta2/) | Llama<br>([**172b**-beta2](https://huggingface.co/llm-jp/llm-jp-3-172b-beta2), [**172b**-beta2-instruct2](https://huggingface.co/llm-jp/llm-jp-3-172b-beta2-instruct2)) | 4,096 | Pre-training: part of [llm-jp-corpus-v3](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)<br>(**1.4T** tokens)<br>Instruction Tuning: [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/), [answer-carefully](https://liat-aip.sakura.ne.jp/wp/answercarefully-dataset/), [magpie-sft-v1.0](https://huggingface.co/datasets/llm-jp/magpie-sft-v1.0), Daring-Anteater, FLAN, ichikara-instruction-format, AutoMultiTurnByCalm3-22B, ramdom-to-fixed-multiturn-Calm3, wizardlm8x22b-logical-math-coding-sft-ja, wizardlm8x22b-logical-math-coding-sft_additional-ja, Synthetic-JP-EN-Coding-Dataset-567k | Research and Development Center for Large Language Models (LLMC) | LLM-jp-3 172B beta2 Terms of Use |
| [LLM-jp-3 172B beta1](https://www.nii.ac.jp/en/news/release/2024/0917.html) | Llama<br>([**172b**-beta1](https://huggingface.co/llm-jp/llm-jp-3-172b-beta1), [**172b**-beta1-instruct](https://huggingface.co/llm-jp/llm-jp-3-172b-beta1-instruct)) | 4,096 | Pre-training: part of [llm-jp-corpus-v3](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)<br>(**0.7T** tokens)<br>Instruction Tuning: [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/), [answer-carefully](https://liat-aip.sakura.ne.jp/wp/answercarefully-dataset/), Dolly Dataset, OASST1, OASST2, Aya Dataset, ichikara-instruction-format, Daring-Anteater, FLAN | Research and Development Center for Large Language Models (LLMC) | LLM-jp-3 172B beta1 Terms of Use |
| [LLM-jp-3 172B alpha](https://llmc.nii.ac.jp/en/topics/llm-jp-3-172b-alpha1-alpha2/) | Llama<br>([**172b**-alpha1](https://huggingface.co/llm-jp/llm-jp-3-172b-alpha1), [**172b**-alpha1-instruct](https://huggingface.co/llm-jp/llm-jp-3-172b-alpha1-instruct), [**172b**-alpha2](https://huggingface.co/llm-jp/llm-jp-3-172b-alpha2), [**172b**-alpha2-instruct](https://huggingface.co/llm-jp/llm-jp-3-172b-alpha2-instruct)) | 4,096 | Pre-training: part of [llm-jp-corpus-v3](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)<br>(alpha1: **0.7T** tokens, alpha2: **1.4T** tokens)<br>Instruction Tuning: [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/), [answer-carefully](https://liat-aip.sakura.ne.jp/wp/answercarefully-dataset/), Dolly Dataset, OASST1, OASST2, Aya Dataset, ichikara-instruction-format, Daring-Anteater, FLAN | Research and Development Center for Large Language Models (LLMC) | Apache 2.0 |
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| | Architecture | Longueur Maximale du Contexte | Données d'entraînement | Développeur | Licence / Conditions d'utilisation |
|:---|:---:|:---:|:---:|:---:|:---:|
| [Sarashina2-8x70B](https://www.sbintuitions.co.jp/news/press/20241108_01/) | Mixtral<br>([8x70b (**465b**)](https://huggingface.co/sbintuitions/sarashina2-8x70b)) | 8,192 | undisclosed | SB Intuitions | Sarashina Model NonCommercial License |
| [Sarashina2-8x70B](https://www.sbintuitions.co.jp/news/press/20241108_01/) | Mixtral<br>([8x70b (**465b**)](https://huggingface.co/sbintuitions/sarashina2-8x70b)) | 8,192 | Sparse Upcycling on Sarashina2 (70B) | SB Intuitions | Sarashina Model NonCommercial License |
| [LLM-jp-3 172B beta2](https://llmc.nii.ac.jp/en/topics/llm-jp-3-172b-beta2/) | Llama<br>([**172b**-beta2](https://huggingface.co/llm-jp/llm-jp-3-172b-beta2), [**172b**-beta2-instruct2](https://huggingface.co/llm-jp/llm-jp-3-172b-beta2-instruct2)) | 4,096 | Pre-training: part of [llm-jp-corpus-v3](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)<br>(**1.4T** tokens)<br>Instruction Tuning: [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/), [answer-carefully](https://liat-aip.sakura.ne.jp/wp/answercarefully-dataset/), [magpie-sft-v1.0](https://huggingface.co/datasets/llm-jp/magpie-sft-v1.0), Daring-Anteater, FLAN, ichikara-instruction-format, AutoMultiTurnByCalm3-22B, ramdom-to-fixed-multiturn-Calm3, wizardlm8x22b-logical-math-coding-sft-ja, wizardlm8x22b-logical-math-coding-sft_additional-ja, Synthetic-JP-EN-Coding-Dataset-567k | Research and Development Center for Large Language Models (LLMC) | LLM-jp-3 172B beta2 Terms of Use |
| [LLM-jp-3 172B beta1](https://www.nii.ac.jp/en/news/release/2024/0917.html) | Llama<br>([**172b**-beta1](https://huggingface.co/llm-jp/llm-jp-3-172b-beta1), [**172b**-beta1-instruct](https://huggingface.co/llm-jp/llm-jp-3-172b-beta1-instruct)) | 4,096 | Pre-training: part of [llm-jp-corpus-v3](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)<br>(**0.7T** tokens)<br>Instruction Tuning: [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/), [answer-carefully](https://liat-aip.sakura.ne.jp/wp/answercarefully-dataset/), Dolly Dataset, OASST1, OASST2, Aya Dataset, ichikara-instruction-format, Daring-Anteater, FLAN | Research and Development Center for Large Language Models (LLMC) | LLM-jp-3 172B beta1 Terms of Use |
| [LLM-jp-3 172B alpha](https://llmc.nii.ac.jp/en/topics/llm-jp-3-172b-alpha1-alpha2/) | Llama<br>([**172b**-alpha1](https://huggingface.co/llm-jp/llm-jp-3-172b-alpha1), [**172b**-alpha1-instruct](https://huggingface.co/llm-jp/llm-jp-3-172b-alpha1-instruct), [**172b**-alpha2](https://huggingface.co/llm-jp/llm-jp-3-172b-alpha2), [**172b**-alpha2-instruct](https://huggingface.co/llm-jp/llm-jp-3-172b-alpha2-instruct)) | 4,096 | Pre-training: part of [llm-jp-corpus-v3](https://gitlab.llm-jp.nii.ac.jp/datasets/llm-jp-corpus-v3)<br>(alpha1: **0.7T** tokens, alpha2: **1.4T** tokens)<br>Instruction Tuning: [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/), [answer-carefully](https://liat-aip.sakura.ne.jp/wp/answercarefully-dataset/), Dolly Dataset, OASST1, OASST2, Aya Dataset, ichikara-instruction-format, Daring-Anteater, FLAN | Research and Development Center for Large Language Models (LLMC) | Apache 2.0 |
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|:---|:---|:---|:---|
| PPO (RLHF) | 2017.07.20 | - | [Proximal Policy Optimization Algorithms](https://arxiv.org/abs/1707.06347) |
| Instruction Tuning<br>(Supervised Fine-tuning; SFT) | 2021.09.03 | ICLR 2022 | [Finetuned Language Models Are Zero-Shot Learners](https://arxiv.org/abs/2109.01652) |
| Sparse Upcycling | 2022.12.09 | ICLR 2023 | [Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints](https://arxiv.org/abs/2212.05055) |
| DPO | 2023.05.29 | NeurIPS 2023 | [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://arxiv.org/abs/2305.18290) |
| SteerLM | 2023.10.09 | EMNLP 2023 (Findings) | [SteerLM: Attribute Conditioned SFT as an (User-Steerable) Alternative to RLHF](https://aclanthology.org/2023.findings-emnlp.754/) |
| ORPO | 2024.03.12 | EMNLP 2024 | [ORPO: Monolithic Preference Optimization without Reference Model](https://arxiv.org/abs/2403.07691) |

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