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'eastus', 'northcentralus', 'westus3', 'westus', 'southcentralus']","task","chat-completion","author","tsuzumi-7b","languages","ja,en","inputModalities","text","outputModalities","datasets",[],"sampleInputData","sampleOutputData","resourceRequirements","stage","Development","systemCatalogData",{"_358":205,"_359":-5,"_360":361,"_362":-5,"_363":364,"_365":366,"_367":-5,"_368":-5,"_369":-5,"_344":370,"_373":-5,"_23":343,"_374":-5,"_375":-5,"_346":376,"_348":377,"_378":-5,"_379":380},"publisher","modelCapabilities","deploymentTypes",[332,333],"license","inferenceTasks",[341],"fineTuningTasks",[341],"inferenceComputeAllowed","fineTuneComputeAllowed","evaluationComputeAllowed",[371,372],"ja","en","summary","textContextWindow","maxOutputTokens",[347],[347],"playgroundRateLimitTier","azureOffers",[381],"standard-paygo","#### モデル名　#Model Name\n\ntsuzumi-7b\n\n#### モデルの詳細 #Model Details\n\n| モデル開発者 | 株式会社 NTTデータ |\n| --- | --- |\n| 利用規約 | ダウンロードページ(「ライセンス規約」) |\n| モデルの説明 | tsuzumiは、NTTが開発した軽量な大規模言語モデルです。日本語と英語の指示に対して従うように学習されています。モデルの特徴、主な用途等は利用ガイドラインを参照ください。利用ガイドのダウンロードページ(「tsuzumi on Azure MaaSユーザーガイド」) |\n| モデル情報 | 下記の表をご覧ください。 |\n| モデルのバリエーション | 現時点でバリエーションはありません。 |\n| モデル入力 | テキストのみが入力可能です。 |\n| モデル出力 | テキストのみが生成可能です。 |\n| モデルのアーキテクチャ | tsuzumiは、自己回帰型トランスフォーマーモデルです。教師ありファインチューニング（SFT）により応答学習を実施しています。 |\n| モデルの学習期間 | tsuzumiの学習期間は2024年8月までであり、2024年5月までの知識を用いて学習されています。 |\n\n#### モデル情報表 #Model Information Table\n\n| 名前 | トレーニングデータ | パラメータ数 | 内容の長さ | GQA | トークン数 |\n| --- | --- | --- | --- | --- | --- |\n| _tsuzumi-7b_ | オンライン公開データと非公開データ | 7B | 8k | ✔ | 1.4T |\n\n#### トレーニングデータ #Training Data\n\n| Data Freshness | 事前学習データは2024年5月までに収集された情報により構成されています。 |\n| --- | --- |\n\n#### 評価結果 #Evaluation Results\n\nこのセクションでは、tsuzumiモデルの日本語ベンチマークにおける結果を報告します。\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n\n

Model	Size	Japanese MT-bench; turn1※　日本語 / 英語
writing	stem	humanities	roleplay	extraction	coding	math	reasoning
tsuzumi-7B	7B	8.6 / 8.2	7.6 / 7.1	8.45 / 8.2	6.3 / 6.05	5.6 / 2.9	2.3 / 2.2	1.1 / 1.1	2.1 / 4.3

\n\n　※1ターンでの評価を採用\n\n#### **入出力例（リアルタイム推論用）#Sample inputs and outputs (for real-time inference)**\n\n設定可能なパラメータ #Supported Parameters\n\n基本パラメータ: #Basic parameters:\n\n| **名前** | **デフォルト** | **説明** |\n| --- | --- | --- |\n| temperature | 0.15 | モデルのランダム性を制御します。低い値でモデルがより決定的な、高い値でモデルがよりランダムな出力を返すようになります。 |\n| max_tokens | 4096 | 生成するトークンの最大数です。 |\n| top_p | 1.0 | 出力をサンプリングする際に考慮する出力候補を絞り込むための累積確率です。 |\n| frequency_penalty | 0.0 | 新しいトークンがこれまでのテキストで存在する頻度に基づきペナルティを与え、同じトークンを繰り返す可能性を抑制します。 |\n| logit_bias | null | モデル出力における特定のトークンの出現確率を変更します。 |\n| logprobs | false | 出力トークンの対数確率を返すかどうかを指定します。 |\n| top_logprobs | 0 | 各トークン位置で出力候補となったトークンを複数確認したい場合に、トークン数を指定します。上位から順に、各トークンの対数確率とともに返却します。 |\n| n | 1 | 生成される応答のバリエーションの数です。 |\n| presence_penalty | 0.0 | これまでのテキストに基づいてトークンにペナルティを与え、新しいトピックについて話す可能性を高めます。 |\n| stop | null | 推論を停止するトークン指定です。 |\n| stream | false | 応答を少しずつ出力するかどうかを指定します。 |\n\n高度なパラメータ #Advanced Parameters – 指定する場合はHTTPリクエストヘッダーに“extra-params: allow”を設定してください:\n\n| **名前** | **デフォルト** | **説明** |\n| --- | --- | --- |\n| min_tokens | 0 | 生成するトークンの最小数です。 |\n| top_k | \\-1 (フィルタなし) | 出力をサンプリングする際に考慮するトークンの個数です。 |\n| repetition_penalty | 1.0 | 繰り返しフレーズに対するペナルティの重みです。高い値ほど類似したフレーズの繰り返しを抑制します。 |\n| length_penalty | 1.0 | 出力長に基づいてシーケンスに与えるペナルティを設定します。 |\n\n#### 入力サンプル #Sample Input\n```\n{\n \"input_data\": {\n \"input_string\": [\n {\n \"role\": \"user\",\n \"content\": \"Linuxの特徴を教えてください。\"\n },\n {\n \"role\": \"assistant\",\n \"content\": \"LinuxはオープンソースのUnixライクなオペレーティングシステムであり、多くのフリーソフトウェアとオープンソースプロジェクトをサポートしています。その特徴には以下が挙げられます:\\n\\n1. カスタマイズ性: LInuxはユーザーによるプログラムや設定ファイルの追加によって様々な方法でカスタマイズできます。これにより個人または特定のニーズに合わせたOS環境を構築することが可能です。\\n\\n2. セキュリティ: Linuxカーネルは頻繁にアップデートされるため、最新のセキュリティパッチとウイルス対策機能を備えており、高い安全性を維持します。\\n\\n3. 移植性: Linuxはさまざまなハードウェアプラットフォーム(サーバ、デスクトップ、タブレットなど)上で動作し、それぞれ異なるアーキテクチャの特性にも対応できるように設計されています。\\n\\n4. コミュニティ: Linuxは広範な開発者およびユーザーネットワークから成る大規模かつ活発なコミュニティを持っています。これらのメンバーは新しいアプリケーションやドライバーを開発・保守したり、問題を解決したり、新技術について議論しあったりします。\"\n },\n {\n \"role\": \"user\",\n \"content\": \"より詳細にお願いします。\"\n }\n ],\n \"parameters\": {\n \"temperature\": 0.7,\n \"top_p\": 0.9\n }\n }\n}\n```\n\n#### 出力サンプル #Sample Output\n\n```\n{\n \"output\": \"Linuxは多くのオープンソースプロジェクトの集合体であり、これらが組み合わさることでより高度な機能を持つ強力なオペレーティングシステムとなります。\\n\\n1. カスタマイズ性: Linuxカーネルには大量の設定可能なパラメータとオプションが含まれており、ユーザーによるプログラムや設定ファイルの追加によってさまざまな方法でシステムを変更できます。これにより、特定のニーズに合わせたOS環境を構築することが可能です。\\n\\n2. セキュリティ: Linuxカーネル自体も頻繁にアップデートされますので、その中で提供されている最新のセキュリティパッチとウイルス対策ツールを利用して安全性を確保します。このように常に新しい脅威から保護されます。\\n\\n3. 移植性: Linuxは異なるハードウェアプラットフォーム上でも動作し、それぞれの構造的特徴に対応できるよう設計されています。これにより、デスクトップコンピュータだけでなく、ノートPCやタブレットなどの他のデバイスにも柔軟かつ効率的にインストール・実行することができます。\\n\\n4. コミュニティ: Linux OS開発者およびユーザーネットワーク内では活発な議論がなされ、問題解決や新技術についての情報共有が行われます。また、バグ修正や新機能の開発など、継続的なサポートと改善活動も行っています。\"\n}\n```\n\n**Descriptions in English**\n\n#### Model Name\n\ntsuzumi-7b\n\n#### Model Details\n\n| Model Developer Name | NTT DATA |\n| --- | --- |\n| Terms of Use | Download(「ライセンス規約」) |\n| Model Description | **tsuzumi** is a lightweight large language model developed by NTT, designed to handle both Japanese and English with high efficiency. Please refer to the User Guide for model features.Link to Download page(「tsuzumi on Azure MaaSユーザーガイド」) |\n| Model Information | See below table |\n| Model Variations | No variations for the moment |\n| Model Input | Models input text only. |\n| Model Output | Models generate text only. |\n| Model Architecture | **tsuzumi** is an auto-regressive language optimized transformer. The tuned versions use supervised fine-tuning (SFT). |\n| Model Dates | **tsuzumi** was trained until 2024/08; Knowledge cutoff is 2024/05. |\n\n#### Model Information Table\n\n| Name | Training Data | Params | Content Length | GQA | Tokens |\n| --- | --- | --- | --- | --- | --- |\n| _Tsuzumi-7b_ | A mix of publicly available online and private data | 7B | 8k | ✔ | 1.4T |\n\n#### Training Data\n\n| Data Freshness | The pretraining data has a cutoff of May 2024. |\n| --- | --- |\n\n#### Evaluation Results\n\nIn this section, we report the results for the tsuzumi models on Japanese standard benchmarks.\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n\n

Model	Size	Japanese MT-bench; turn1※　Japanese / English
writing	stem	humanities	roleplay	extraction	coding	math	reasoning
tsuzumi-7B	7B	8.6 / 8.2	7.6 / 7.1	8.45 / 8.2	6.3 / 6.05	5.6 / 2.9	2.3 / 2.2	1.1 / 1.1	2.1 / 4.3

\n　※Evaluated in 1 turn only\n\n#### **Sample inputs and outputs (for real-time inference)**\n\nSupported Parameters\n\n\\# Basic parameters:\n\n| **name** | **Defaults to** | **Explanation** |\n| --- | --- | --- |\n| temperature | 0.15 | Controls randomness in the model. Lower values will make the model more deterministic and higher values will make the model more random. |\n| max_tokens | 4096 | The maximum number of tokens to generate. |\n| top_p | 1.0 | The cumulative probability of parameter highest probability vocabulary tokens to keep for nucleus sampling. |\n| frequency_penalty | 0.0 | Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim. |\n| logit_bias | null | Modify the likelihood of specified tokens appearing in the completion. |\n| logprobs | false | Whether to return log probabilities of the output tokens or not. |\n| top_logprobs | 0 | An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. |\n| n | 1 | The number of generated response variations. |\n| presence_penalty | 0.0 | Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics. |\n| stop | null | Specification for stop words. |\n| stream | false | Whether response is returned in partial message deltas. |\n\n\\# Advanced Parameters – To specify, set “extra-params: allow” in your HTTP request header:\n\n| **name** | **Defaults to** | **explanation** |\n| --- | --- | --- |\n| min_tokens | 0 | The minimum number of tokens to generate. |\n| top_k | \\-1 (no filter) | The number of highest probability vocabulary tokens to keep for top-k-filtering. |\n| repetition_penalty | 1.0 | The weight of penalty for repeated phrases. Higher values will suppress repeating similar phrases. |\n| length_penalty | 1.0 | Penalizes sequences based on their length. |\n\n#### Sample Input\n\n```\n{\n \"input_data\": {\n \"input_string\": [\n {\n \"role\": \"user\",\n \"content\": \"Linuxの特徴を教えてください。\"\n },\n {\n \"role\": \"assistant\",\n \"content\": \"LinuxはオープンソースのUnixライクなオペレーティングシステムであり、多くのフリーソフトウェアとオープンソースプロジェクトをサポートしています。その特徴には以下が挙げられます:\\n\\n1. カスタマイズ性: LInuxはユーザーによるプログラムや設定ファイルの追加によって様々な方法でカスタマイズできます。これにより個人または特定のニーズに合わせたOS環境を構築することが可能です。\\n\\n2. セキュリティ: Linuxカーネルは頻繁にアップデートされるため、最新のセキュリティパッチとウイルス対策機能を備えており、高い安全性を維持します。\\n\\n3. 移植性: Linuxはさまざまなハードウェアプラットフォーム(サーバ、デスクトップ、タブレットなど)上で動作し、それぞれ異なるアーキテクチャの特性にも対応できるように設計されています。\\n\\n4. コミュニティ: Linuxは広範な開発者およびユーザーネットワークから成る大規模かつ活発なコミュニティを持っています。これらのメンバーは新しいアプリケーションやドライバーを開発・保守したり、問題を解決したり、新技術について議論しあったりします。\"\n },\n {\n \"role\": \"user\",\n \"content\": \"より詳細にお願いします。\"\n }\n ],\n \"parameters\": {\n \"temperature\": 0.7,\n \"top_p\": 0.9\n }\n }\n}\n```\n\n\n#### Sample Output\n\n```\n{\n \"output\": \"Linuxは多くのオープンソースプロジェクトの集合体であり、これらが組み合わさることでより高度な機能を持つ強力なオペレーティングシステムとなります。\\n\\n1. カスタマイズ性: Linuxカーネルには大量の設定可能なパラメータとオプションが含まれており、ユーザーによるプログラムや設定ファイルの追加によってさまざまな方法でシステムを変更できます。これにより、特定のニーズに合わせたOS環境を構築することが可能です。\\n\\n2. セキュリティ: Linuxカーネル自体も頻繁にアップデートされますので、その中で提供されている最新のセキュリティパッチとウイルス対策ツールを利用して安全性を確保します。このように常に新しい脅威から保護されます。\\n\\n3. 移植性: Linuxは異なるハードウェアプラットフォーム上でも動作し、それぞれの構造的特徴に対応できるよう設計されています。これにより、デスクトップコンピュータだけでなく、ノートPCやタブレットなどの他のデバイスにも柔軟かつ効率的にインストール・実行することができます。\\n\\n4. コミュニティ: Linux 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