'hf' 검색 결과

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.

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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.

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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.

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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.

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

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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.

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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.

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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.

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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.

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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.

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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.

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

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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.

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React Hook Form v7 (RHF、非制御フォームライブラリ) リファレンス。別名 rhf / React-Hook-Form。 useForm, register, handleSubmit, watch, setValue, getValues, getErrors, reset、 Controller, useFieldArray / FieldArray, formState / FormState, ErrorMessage、 resolver (Zod / Yup / Joi)、バリデーション、Server Actions / useActionState、 DevTools (@hookform/devtools)、TypeScript 型。

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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.

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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.

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

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Hugging Face Hub에서 로컬 언어 모델을 찾고, 메모리에 맞는 GGUF 파일을 고르도록 돕습니다. 파일 용량을 비교하고 llama.cpp 실행 명령을 준비합니다.

• 메모리에 맞는 로컬 모델을 찾습니다
• GGUF 종류와 파일 용량을 비교합니다
• 로컬 채팅 서버 실행을 준비합니다

Figma 디자인을 원래 모습에 충실한 실제 서비스용 프런트엔드 코드로 변환합니다. 화면 디자인 프레임을 프런트엔드 담당 AI 에이전트에게 직접 전달해 구현에 활용합니다.

• Figma에서 만든 화면을 구현합니다
• AI 에이전트에게 디자인을 전달합니다
• 디자인의 시각적 모습을 코드로 재현합니다

Turn real product reference photos into three ecommerce images: a clean main shot, a feature highlight, and a lifestyle scene. The workflow prioritizes product consistency and also creates an image manifest and a preview gallery.

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• Creating a main product image from a reference photo
• Showing one product feature in a dedicated image
• Creating a realistic product usage scene

Guide GAIA benchmark runs from environment checks and cost estimates to result comparison. Create a signed submission package compatible with HAL, including agent traces, metadata, and an audit report that checks for benchmark exploitation.

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• Check readiness for a GAIA benchmark run
• Estimate costs and run an evaluation
• Package existing results for submission

Explore a knowledge graph, a network of entities and relationships, from a chosen starting point. Rank relevant paths and summarize dependencies or indirect connections to help understand code structure and assess the impact of changes.

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• Assessing the impact of changing a component
• Tracing dependency chains
• Discovering indirect connections between entities

Mix audio already placed in HyperFrames compositions. Adjust fades, volume changes, and effects; reduce music frequencies that compete with speech; and apply shared processing to multiple tracks through a group with common volume controls.

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• Make narration easier to hear over background music
• Add fades and crossfades to placed audio
• Balance uneven voice levels and adjust tone

Use Hugging Face Transformers to load pretrained models, generate text, and classify text, images, or audio. Prepare model inputs, control generation settings, and fine-tune models on custom datasets with the Trainer API.

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• Prototype text, image, or audio classification
• Configure text generation and conversational responses
• Fine-tune a model on a custom dataset