Together AI と Adaption のパートナーシップ発表
Together AI と Adaption の提携により、Adaptive Data プラットフォーム上で Together Fine-Tuning がネイティブに利用可能となり、データ最適化からモデル微調整・評価までのワークフローが統合された。
キーポイント
プラットフォーム連携の開始
Together AI の「Together Fine-Tuning」機能が Adaption の「Adaptive Data」にネイティブ統合され、ユーザーはデータ最適化後、直接ハイパーパラメータを調整して微調整を実行できるようになった。
データ品質の劇的向上
Adaption は共同創設者の Sara Hooker 氏らが率いる企業であり、同社のデータ最適化技術により、早期導入で平均 82% のデータ品質向上を実現している。
シームレスな実験ワークフロー
データの分析・適応から微調整、評価結果の可視化（勝率、ロス関数など）、そして Together AI 上の推論サービスへのデプロイまでが一貫した環境で完結する。
重要な引用
Adaption describes this set of capabilities as bringing data optimization techniques typically reserved for frontier labs to everyday builders
Together Fine-Tuning gives Adaptive Data users the infrastructure to turn shaped datasets into stronger, more reliable open models.
Its support for LoRA and full fine-tuning, large open models, and experiment visibility helps our users adapt quickly
影響分析・編集コメントを表示
影響分析
この提携は、LLM の実務利用における最大のボトルネックである「高品質なトレーニングデータの準備と検証」プロセスを劇的に簡素化する画期的な進展です。これまで個別に管理する必要があったデータエンジニアリングとモデル微調整のインフラが統合されることで、開発者は技術的複雑さに悩まされずに、より迅速かつ信頼性の高いオープンソースモデルのカスタマイズを実現できるようになります。
編集コメント
データの前処理とモデル学習のインフラを統合する動きは、LLM 開発の民主化において極めて重要なステップです。特に、データ品質の向上がモデル性能に直結することを裏付ける数値（82%）と、実装コストの低下が期待されるため、実務家にとって即効性のあるニュースと言えます。
Together Fine-Tuning now natively available in Adaptive Data by Adaption
We’re excited to partner with Adaption to make Together Fine-Tuning available in Adaptive Data. Adaption is co-founded by Sara Hooker and Sudip Roy, both former leaders at Cohere and Google DeepMind veterans. Adaptive Data addresses the data challenges of modern model training by helping teams analyze dataset structure, adapt examples, evaluate quality, and export model-ready data. Adaption describes this set of capabilities as bringing data optimization techniques typically reserved for frontier labs to everyday builders, and reports an average 82% increase in data quality across early deployments.
With this integration, Adaption users can connect their Together AI account to achieve the fastest time to high-quality, fine-tuned model through a seamless experimentation workflow. In Adaption, the user optimizes their training dataset, then directly executes Together fine-tuning on that data with optimized hyperparameters as a starting point. Once trained, the fine-tuned model is deployed for evaluation and eval results are shown to the user; from there, users can deploy the model on Together AI’s high-performance inference service.
Together Fine-Tuning gives Adaptive Data users the infrastructure to turn shaped datasets into stronger, more reliable open models. Its support for LoRA and full fine-tuning, large open models, and experiment visibility helps our users adapt quickly, understand what changed, and improve performance against target behaviors. - Sara Hooker, Co-founder & CEO, Adaption
Why Together Fine-Tuning
Together Fine-Tuning is the leading open source post-training and inference provider built for teams that want to customize leading open models to their data without managing the infrastructure themselves. Together AI
The platform supports leading open models, including models over 100B parameters such as Kimi K2.5, GLM 5.1, or Qwen 3.5-397B, across structured tool use, reasoning, and vision-language setups. Users can fine-tune on large datasets, estimate job cost before training starts, track ETA during a run, and export models directly to Hugging Face Hub.
With this integration, datasets shaped in Adaptive Data can move directly into Together Fine-Tuning workflows. Adaptive Data improves the upstream dataset; Together Fine-Tuning turns that dataset into specialized model behavior.
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Premium cinematic video generation with native audio and lifelike physics.
Performance & Scale
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Infrastructure
Best for
- Faster processing speed (lower overall query latency) and lower operational costs
- Execution of clearly defined, straightforward tasks
- Function calling, JSON mode or other well structured tasks
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Build
Benefits included:
- ✔ Up to $15K in free platform credits*
- ✔ 3 hours of free forward-deployed engineering time.
Funding: Less than $5M
Build
Benefits included:
- ✔ Up to $15K in free platform credits*
- ✔ 3 hours of free forward-deployed engineering time.
Funding: Less than $5M
Build
Benefits included:
- ✔ Up to $15K in free platform credits*
- ✔ 3 hours of free forward-deployed engineering time.
Funding: Less than $5M
Think step-by-step, and place only your final answer inside the tags *<answer>* and *</answer>*. Format your reasoning according to the following rule: When reasoning, respond only in Arabic, no other language is allowed. Here is the question:
Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?
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8S
DeepSeek R1
Premium cinematic video generation with native audio and lifelike physics.
DeepSeek R1
8S
Audio Name
Audio Description
0:00
Premium cinematic video generation with native audio and lifelike physics.
8S
DeepSeek R1
Premium cinematic video generation with native audio and lifelike physics.
Performance & Scale
Body copy goes here lorem ipsum dolor sit amet
- Bullet point goes here lorem ipsum
- Bullet point goes here lorem ipsum
- Bullet point goes here lorem ipsum
Infrastructure
Best for
- Faster processing speed (lower overall query latency) and lower operational costs
- Execution of clearly defined, straightforward tasks
- Function calling, JSON mode or other well structured tasks
List Item #1
- Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt.
- Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt.
- Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt.
List Item #1
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
Build
Benefits included:
- ✔ Up to $15K in free platform credits*
- ✔ 3 hours of free forward-deployed engineering time.
Funding: Less than $5M
Build
Benefits included:
- ✔ Up to $15K in free platform credits*
- ✔ 3 hours of free forward-deployed engineering time.
Funding: Less than $5M
Build
Benefits included:
- ✔ Up to $15K in free platform credits*
- ✔ 3 hours of free forward-deployed engineering time.
Funding: Less than $5M
Think step-by-step, and place only your final answer inside the tags *<answer>* and *</answer>*. Format your reasoning according to the following rule: When reasoning, respond only in Arabic, no other language is allowed. Here is the question:
Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?
XX
Title
Body copy goes here lorem ipsum dolor sit amet
XX
Title
Body copy goes here lorem ipsum dolor sit amet
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Title
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