72건 · 관련도순
Selects a fine-tuning technique (SFT, DPO, RLVR, or RLAIF) for the user's use case and validates it against the selected model's available recipes. Use when the user has decided to finetune and needs to choose a technique, or when the technique needs to be validated against a model. Requires a base model to already be selected (via model-selection skill).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generates code that fine-tunes a base model using SageMaker serverless training jobs. Use when the user says "start training", "fine-tune my model", "I'm ready to train", or when the plan reaches the finetuning step. Supports SFT, DPO, RLVR, and RLAIF trainers, including RLVR Lambda reward function and RLAIF custom prompt creation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generates code that transforms datasets between ML schemas for model training or evaluation. Use when the user says "transform", "convert", "reformat", "change the format", or when a dataset's schema needs to change to match the target format — always use this skill for format changes rather than writing inline transformation code. Supports OpenAI chat, SageMaker SFT/DPO/RLVR/RLAIF, HuggingFace preference, Bedrock Nova, VERL, and custom JSONL formats from local files or S3.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Validates dataset formatting and quality for SageMaker model fine-tuning (SFT, DPO, or RLVR). Use when the user says "is my dataset okay", "evaluate my data", "check my training data", "I have my own data", or before starting any fine-tuning job. Detects file format, checks schema compliance against the selected model and technique, and reports whether the data is ready for training or evaluation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert knowledge for Azure Blob Storage development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using tiers/archival, NFS/SFTP, BlobFuse, Storage Mover, SAS/RBAC auth, or static website hosting, and other Azure Blob Storage related development tasks. Not for Azure Files (use azure-files), Azure Queue Storage (use azure-queue-storage), Azure Table Storage (use azure-table-storage).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Train and fine-tune transformer language models using TRL (Transformers Reinforcement Learning). Supports SFT, DPO, GRPO, KTO, RLOO and Reward Model training via CLI commands.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Train or fine-tune language models with TRL or Unsloth on Hugging Face Jobs, including SFT, DPO, GRPO, reward models, and GGUF conversion. Use for cloud LLM training; use huggingface-vision-trainer for vision tasks.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Routes agents working with Axolotl config-driven LLM fine-tuning, datasets, SFT, preference tuning, GRPO/EBFT, model adapters, distributed training, CLI operations, and troubleshooting.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Trigger this skill when the user wants to train, fine-tune, or adapt Gemma models (e.g. SFT, DPO, RLHF, Reward Modeling) on local hardware. Covers TRL, Unsloth, dataset preparation, validation, and GGUF/LiteRT conversion.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Chooses DPO, GRPO/RLVR, reward models, and over-optimization controls. Use when adapting a pretrained or SFT model with preference or verifiable reward signals.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Adapts LLMs: SFT dataset loss masking, LoRA and QLoRA, full FT vs PEFT, distillation, pruning, tokenizer fragmentation. Use when fine-tuning or compressing a model.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
End-to-end system for creating supervised fine-tuning datasets from books and training style-transfer models. Covers text extraction, intelligent segmentation, synthetic instruction generation, Tinker-compatible output, LoRA training, and validation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Fine-tune large language models — data curation, SFT, parameter-efficient methods (LoRA), evaluation, and avoiding common training failures. Use when a base model needs task-specific behavior or domain knowledge.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use this when the user wants to generate, normalize, verify, deduplicate, or export training datasets for Codex, Antigravity, or Claude Code from topics, URLs, reference material, web research, or existing JSONL/CSV files. Supports SFT and DPO workflows, custom export schemas, and deterministic local pipeline scripts.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
TRL: SFT, DPO, PPO, GRPO, reward modeling for LLM RLHF.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use Exasol `EXPORT` and `EXPORT INTO` SQL plus `exapump export` local file workflows for moving data out of Exasol. Covers native CSV/FBV export, local CSV/Parquet exports with exapump, `CREATE CONNECTION` connection objects for export, FTP, SFTP, HTTP, HTTPS, and cloud targets on S3, Azure Blob Storage, and Google Cloud Storage (GCS), reject limits, and export credential patterns.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Train and fine-tune transformer language models using TRL (Transformers Reinforcement Learning). Supports SFT, DPO, GRPO, KTO, RLOO and Reward Model training via CLI commands.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Set up and run the full RLHF pipeline (SFT, reward model training, RL from reward model) using the Tinker API. Use when the user wants to do RLHF, train a reward model, or run the full preference-based RL pipeline.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build training datasets for LLM specialization from production data, frontier model distillation, and synthetic bootstrapping. Use when formatting production logs into SFT data, distilling from frontier APIs, or preparing data for fine-tuning. Covers JSONL formatting, data quality validation, deduplication, and train/eval splitting.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
OpenAI API (developers.openai.com) の Evals / Fine-tuning リファレンス。 Evals API, Datasets, graders（string check / text similarity / model / python / multi）, agent evals, trace grading, external models, Promptfoo（移行先）, red teaming, SFT, DPO, RFT, vision fine-tuning, distillation, prompt optimizer, fine-tuning best practices。 Evals は 2026-11-30 停止予定、セルフサービス fine-tuning は段階的廃止中。
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Apple オンデバイス機械学習フレームワークリファレンス。 Core AI (.aimodel) / Core ML / Create ML / Vision / Natural Language / Speech / Media Intelligence / Music Understanding。 AIModel, InferenceFunction, MLModel, MLModelConfiguration, MLMultiArray, MLTensor, MLComputeUnits, MLImageClassifier, MLTextClassifier, MLDataTable, VNCoreMLRequest, VNImageRequestHandler, VNRecognizeTextRequest, RecognizeTextRequest, NLTagger, NLEmbedding, NLLanguageRecognizer, SpeechAnalyzer, SpeechTranscriber, SFSpeechRecognizer, SFSpeechAudioBufferRecognitionRequest, SFTranscription, FaceGroupAnalyzer, MusicUnderstandingSession。
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.