モデルを切り替えるたびに会話の文脈を再計算し直すの、実はもったいないかもしれません。 タイトル: Cross-Model KV Cache Transfer in LLM Families: A Closed-Form Linear Mapping for Prefill Reuse URL: ❓ そもそも何をする研究？ 💡 NVIDIAの研究チームが、あるサイズのLLMが計算したKVキャッシュを、別サイズの同系統モデルにそのまま「翻訳」して使い回す手法を提案しました。学習不要、閉形式のリッジ回帰だけで変換できます。 ❓ なぜモデルサイズをまたいで使い回したいの？ 💡 実運用ではコストと品質のバランスを取るために、小さいモデルから大きいモデルへエスカレーションしたり、会話途中でモデルを切り替えたりします。そのたびにゼロから文脈を再計算（re-prefill）するのは無駄が多いためです。 ❓ 精度は落ちないの？ 💡 良い組み合わせでは元モデル単体の73〜98%の精度を維持します。相性の悪いペアでは大きく劣化しますが、小さなMLPを追加するだけでHellaSwagが最大36.8ポイント回復しました。 ❓ どれくらい速くなるの？ 💡 再prefillと比べて2.7〜25倍高速。32Kトークンの長い文脈では最大25倍の差がつき、10ターン程度の会話でも精度の劣化はごくわずかでした。 #LLM# #KVキャッシュ#

# Learning Palantir Foundry 🚀 The first move that turns a dataset into a business object. How you design Object Types largely decides downstream app performance and UX. 📌 Title and Feature URL Title: オブジェクトタイプ URL: 📝 Overview An Object Type defines the schema for a real-world entity or event. A single occurrence is an object instance (e.g., employee "Melissa Chang"), while a group is an object set (e.g., all tenured employees). This mirrors how datasets handle rows and filtered row collections. 🔧 How It Works - Primary keys and identity: objects need a primary key to uniquely identify instances. Mapping a data source to the object type lets you create and display objects in applications. - Properties: define an object's characteristics, with options such as edit-only properties, required properties, and shared properties reused across multiple object types. - Property types: support time series data, geospatial information, and struct types (nested, complex properties). - Display and search: title/display settings and search indexing improve discoverability inside apps. - Value Types: custom value types with versions, permissions, and constraints standardize representation across the ontology. 🛠 Practical Usage - Connect an employee directory or enterprise data to an Employee object type, converting raw datasets into actionable ontology instances. - Nail down primary key design first and index for search to secure downstream app performance and UX. - Use struct properties to auto-map hierarchical data, combined with shared properties for reuse. 🎯 Use Cases - Turn a customer master into a Customer object so the whole company shares one identity. - Model sensor-equipped assets with time series properties to retain operating history. - Model sites and stores with geospatial properties for map-based search and aggregation. ⚠️ Caveats - Primary key design, property types, and search indexing largely determine later app performance and UX, so treat them as your most important modeling decisions. - You must correctly map a data source to the object type before objects can be created or displayed. #PalantirFoundry# #Ontology#

# Learning Palantir Foundry 🚀 Put business logic right on the ontology. Functions cure the "numbers don't match across departments" problem by centralizing logic in one place. 📌 Title and Feature URL Title: ファンクション URL: 📝 Overview Functions let you write server-side logic that executes in isolated environments, powering operational apps like dashboards and decision-support tools. They are designed to work with Foundry ontologies, so they can read object properties, traverse links, and perform flexible ontology edits. 🔧 How It Works - Supported languages: TypeScript (full feature support) and Python (beta, with growing support especially for serverless and deployed execution). - Serverless execution: spins up on demand when invoked and bills only during execution, with a 60-second total wall-clock timeout (30s CPU plus a 30s network buffer). Multiple versions can run simultaneously, making upgrades safer. - Deployed execution: reserves dedicated resources for cases serverless cannot meet, runs a single version at a time, and bills continuously while deployed. - Capability differences: ontology read/write, Workshop integration, and external API calls work in both languages. Pipeline Builder is Python, while model embedding and semantic search are TypeScript. 🛠 Practical Usage - Derived properties: display function-computed values as table columns. - Function-backed Actions: implement complex edits spanning multiple objects. - Workshop integration: run functions to compute or display variables. - API gateway: invoke query functions programmatically to reuse the same logic everywhere. 🎯 Use Cases - Implement derived-KPI logic once and return identical results to Workshop, OSDK, and the API. - Query external systems to enrich ontology objects. - Build complex validation or bulk updates as function-backed Actions. ⚠️ Caveats - The 60-second timeout applies uniformly across execution modes, so optimize for efficiency. - Available capabilities depend on the invocation context (for example, model embedding and semantic search are TypeScript only), so decide on language early. #PalantirFoundry# #DataEngineering#

# Learning Palantir Foundry 🚀 Action Types are what decisively separate Foundry from read-only BI. Approvals and assignments become safe, validated, structured writes. 📌 Title and Feature URL Title: アクションタイプ URL: 📝 Overview An Action Type defines a set of changes a user can apply to ontology objects, properties, and links in a single transaction. It encapsulates both the data modifications and any side effects triggered on submission, letting users think in terms of overall goals rather than individual property edits. 🔧 How It Works - Write-back to the ontology: when an action runs, all changes are committed to the ontology and reflected across every app. The latest object data, including user edits, is captured in the object type's write-back dataset. - Parameters and defaults: parameters standardize input, supporting default values, filtered dropdown results, and overrides. - Rules: define when and how an action executes, including object relationships and property constraints. - Submission criteria and validation: validation rules control execution eligibility and error handling before changes persist. - Action logs: a full audit trail of every executed action supports accountability and compliance. 🛠 Practical Usage - Run an "Assign Employee" style action that changes a role property, auto-creates a manager-employee link, and notifies stakeholders in one transaction. - Embed submission criteria like "only a director may submit amounts over 1M yen" as validation, replacing Excel-plus-email approvals with structured operations. - Reuse the same validation logic and workflow consistently across every user-facing app. 🎯 Use Cases - Standardize status changes, approvals, and assignments as permissioned, criteria-bound operations. - Let non-technical users safely execute multi-step changes spanning several objects. - Use the action log of every operation as an audit trail for internal controls. ⚠️ Caveats - Actions execute only after passing their validation rules, so submission-criteria design drives the quality of your controls. - Changes propagate immediately across the ontology and all apps, so do not leave rule and parameter design ambiguous. #PalantirFoundry# #Ontology#

#RESET公演# ありがとうございました🎤💚 美容院に行ったのでサラッサラストレートで パフォーマンスしました〜！！！ この公演とストレートヘアの相性も良い気がする😳❤️ とっても熱いコールや団扇ペンライト❕ 沢山ありがとうございました、好きish💘

#RESET公演# ありがとうございました！ めちゃめちゃ楽しくて最高でした😭‼️ メンバーみんなと目を合わせて笑い会う時間。𝑯𝒂𝒑𝒑𝒚 𝓽𝓲𝓶𝓮すぎた😆🌟 9月いいスタート‼️♪( ◜ω◝و(و " ファンの皆さんにも感謝です🙏🏻 改めて９月もよろしくお願いします！

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#RESET公演# ありがとうございました〜！❤️‍🔥 今日は久しぶりのポジションでした👑🎀 中盤曲の表現を少し変えてみたよ~！ ここのポジションも楽しくてすき！！！ 今月ラストでした❕ 団扇やペンライトもありがとう⟡.·

#RESET公演# ありがとうございました！ 新ポジたのしかったーー🩵🪽 せっかく逆転王子様やれたのに 写真撮り忘れちゃったから 次いつか出た時の私は撮ってね🥺託す ゆーゆちゃん初日お疲れさま💕 堂々としてて素敵だったね！ オンデマンドコメント2人で撮ったので 見てくださーーい♪