「hf」の検索結果

Prepare for Anthropic-specific technical interviews covering Constitutional AI, RLHF, interpretability, scaling laws, MCP, agentic systems, and AI safety. Activate on "anthropic interview", "constitutional AI prep", "alignment interview", "safety interview", "RLHF opinion", "interpretability prep", "anthropic technical". NOT for general ML interview prep, system design interviews, coding challenges, or behavioral interview practice.

日本語の概要は準備中です。原文の説明を表示しています。

HuggingFace hf CLI: search/download/upload models, datasets.

日本語の概要は準備中です。原文の説明を表示しています。

Turn briefs, mockups, and product context into Stitch UI screens, design systems, variants, prototypes, Tailwind-friendly HTML, and screenshots. Use when the user wants to explore a screen, edit an existing Stitch project, compare visual directions, build a multi-screen prototype, or save local design/code-export artifacts from natural-language input.

日本語の概要は準備中です。原文の説明を表示しています。

Legacy compatibility alias for StitchFlow. Use when a user explicitly references stitch-design-local, or when older prompts and setups still call that skill name.

日本語の概要は準備中です。原文の説明を表示しています。

Ajuste fino de LLMs usando reinforcement learning com TRL - SFT para ajuste de instruções, DPO para alinhamento de preferências, PPO/GRPO para otimização de recompensas e treinamento de modelo de recompensas. Use quando precisar de RLHF, alinhar modelo com preferências ou treinar a partir de feedback humano. Funciona com HuggingFace Transformers.

日本語の概要は準備中です。原文の説明を表示しています。

Fornece orientação para treinamento RL agentico nativo do PyTorch usando torchforge, biblioteca da Meta que separa infraestrutura de algoritmos. Use quando você quer abstrações limpas de RL, fácil experimentação de algoritmos, ou treinamento escalável com Monarch e TorchTitan.

日本語の概要は準備中です。原文の説明を表示しています。

Fornece orientação para treinar LLMs com aprendizado por reforço usando verl (Volcano Engine RL). Use ao implementar RLHF, GRPO, PPO ou outros algoritmos de RL para pós-treinamento de LLMs em escala com backends de infraestrutura flexíveis.

日本語の概要は準備中です。原文の説明を表示しています。

Universeller Skill für LLM-Navigation, Orientierung und Ausfallsicherheit. Stellt aktives Wayfinding, Selbstorientierung und Wiederherstellungsheuristiken bereit, wenn Agenten mit Kontextdrift, fehlschlagenden Werkzeugen, Schleifen oder Sackgassen konfrontiert sind. Enthält die synonymen Strategien survival-routing, dead-reckoning, pathfinder-routing und celestial-routing.

日本語の概要は準備中です。原文の説明を表示しています。

HuggingFace hf CLI: search/download/upload models, datasets.

日本語の概要は準備中です。原文の説明を表示しています。

Set up an isolated Python environment for SageMaker / AWS work, with the right Python version and current boto3. Use this skill whenever Python code will be executed for a SageMaker deployment, training job, or any AWS automation — including when about to run `pip install`, when about to invoke `boto3`, when creating or activating a virtualenv, or when the user asks to "set up the environment". Never use system Python and never `pip install` into it. Always isolate. This skill prevents the most common failure modes: wrong Python version, dependency conflicts, and stale SDKs.

日本語の概要は準備中です。原文の説明を表示しています。

Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host, serve, or expose a model on SageMaker or AWS — including phrases like "deploy a model", "host this LLM on AWS", "serve this embedding model", "deploy a reranker", "deploy a text-to-image / diffusion model", "host this for async inference", "create an endpoint", "serve my fine-tuned model", or any request that involves making a model available for inference on AWS. Use this even when the user is vague (e.g. "I just want to get this running on AWS, you figure it out"). Works for text-generation LLMs, embedding models, rerankers, classifiers, text-to-image / diffusion models — picks the right serving stack and chooses between real-time and async inference. This is the entry-point skill for SageMaker deployment work — it asks clarifying questions, picks a deployment pathway, and coordinates the other deployment skills.

日本語の概要は準備中です。原文の説明を表示しています。

Create a SageMaker endpoint (real-time, real-time scale-to-zero, or async) with autoscaling, CloudWatch alarms, and tagging enabled by default. Use this skill whenever about to create a SageMaker endpoint, write deployment code that calls `create_endpoint`, or finalize a deployment after the image URI and IAM role are known. Provides deploy.py for real-time endpoints, deploy_ic.py for real-time endpoints that scale to zero instances via inference components, and deploy_async.py for async endpoints (also scale-to-zero). This is the last step in the SageMaker deployment workflow. Never generate a bare `create_endpoint` call without these defaults — endpoints without autoscaling or alarms are demos, not deployments.

日本語の概要は準備中です。原文の説明を表示しています。

Discover the user's local AWS context (active profile, region, account ID, caller identity) at the start of any AWS task. Use this skill before any other AWS work — deploying to SageMaker, creating resources, calling AWS APIs, or anything that touches an AWS account. Use it especially when the user has not specified a region or profile explicitly, when they say things like "use my AWS account", "deploy to AWS", "use my profile", or when about to make any AWS CLI or SDK call. Never guess the region or account ID — always use this skill to read it from the local configuration first.

日本語の概要は準備中です。原文の説明を表示しています。

Ensure a usable SageMaker execution role exists before deploying or training. Use this skill whenever about to create a SageMaker endpoint, model, training job, or any resource that requires an execution role. Use it especially when the user has not provided a role ARN explicitly, when scripts are about to call `iam:CreateRole`, or when an AccessDenied error mentions an IAM action. Never blindly call `iam:CreateRole` — always check for existing roles first. This skill prevents the most common SageMaker deployment failure: trying to create IAM resources from an SSO principal that has no IAM write permissions.

日本語の概要は準備中です。原文の説明を表示しています。

HuggingFace hf CLI: search/download/upload models, datasets.

日本語の概要は準備中です。原文の説明を表示しています。

Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.

日本語の概要は準備中です。原文の説明を表示しています。

Hugging Face Hub CLI (hf) — search, download, and upload models and datasets, manage repos, query datasets with SQL, deploy inference endpoints, manage Spaces and buckets.

日本語の概要は準備中です。原文の説明を表示しています。

HuggingFace hf CLI: search/download/upload models, datasets.

日本語の概要は準備中です。原文の説明を表示しています。

Prepare for Anthropic-specific technical interviews covering Constitutional AI, RLHF, interpretability, scaling laws, MCP, agentic systems, and AI safety. Activate on "anthropic interview", "constitutional AI prep", "alignment interview", "safety interview", "RLHF opinion", "interpretability prep", "anthropic technical". NOT for general ML interview prep, system design interviews, coding challenges, or behavioral interview practice.

日本語の概要は準備中です。原文の説明を表示しています。

llama.cpp local GGUF inference + HF Hub model discovery.

日本語の概要は準備中です。原文の説明を表示しています。

Reference-product ecommerce image workflow for generating a compact set of product-faithful main, feature, and lifestyle images from real product reference photos. V1 requires uploaded product imagery and intentionally defers brief-only concept generation and platform-specific batch exports.

日本語の概要は準備中です。原文の説明を表示しています。

Map a codebase into feature-grouped flowcharts, identify duplicated concerns across features, and propose a unified architecture. Use when asked to "find the ideal path," unify duplicated systems, or audit architecture before a refactor. Emits a proposed unified flowchart plus per-system /make-plan prompts.

日本語の概要は準備中です。原文の説明を表示しています。

Pathfinder traversal of the knowledge graph starting from a seed entity

日本語の概要は準備中です。原文の説明を表示しています。

Use when audio already placed in a HyperFrames composition needs to be mixed: fade-in/fade-out, crossfade, track gain or volume, volume automation, ducking, a music bed that fights a voiceover (voiceover carve), effects on a track (EQ, compressor, limiter, gate, saturation, delay, reverb, chorus, phaser, bitcrush), automation envelopes drawn on a track's volume or any effect parameter, or one submix bus carrying a chain, a fader and an automation clock for several tracks at once (`<hf-audio-group>`). Don't use for sourcing or generating audio — finding BGM, SFX, or making a voiceover is `/media-use`. Don't use for clip timing or track layout, which is `/hyperframes-core`.

日本語の概要は準備中です。原文の説明を表示しています。