「generalization」の検索結果

Scaffold a new training recipe for the Tinker cookbook following repo conventions. Use when the user wants to create a new recipe, training script, or experiment.

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 and run multi-turn RL training for interactive environments (terminal tasks, tool use, search/RAG, games) using the Tinker API. Use when the user wants multi-turn RL, agentic training, tool-use RL, or interactive environment training.

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, update, or organize Claude Code skills in this repo. Use when adding a new skill, reviewing existing skills for consistency, or maintaining the skill taxonomy.

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 and run knowledge distillation (on-policy, off-policy, or multi-teacher) from a teacher model to a student model using the Tinker API. Use when the user wants to distill knowledge, compress models, or train a student from a teacher.

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。

Guide for dataset construction — SupervisedDatasetBuilder, RLDatasetBuilder, ChatDatasetBuilder, and custom dataset creation from JSONL, HuggingFace, or conversation data. Use when the user asks about datasets, data loading, data preparation, or custom data formats.

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。

Guide for testing conventions and CI pipelines — unit tests, integration smoke tests, pytest markers, and GitHub Actions workflows. Use when the user asks about testing, CI, running tests, or adding tests for a recipe.

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 and run reinforcement learning with verifiable rewards (RLVR/GRPO) for math, code, or custom environments using the Tinker API. Use when the user wants to do RL training, GRPO, reward-based optimization, or train with verifiable rewards.

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 and run supervised fine-tuning (SFT) on instruction or chat datasets using the Tinker API. Use when the user wants to do instruction tuning, chat fine-tuning, or supervised learning.

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。

Guide for choosing models in Tinker — available model families, model types (base, instruction, reasoning, hybrid, vision), architecture (dense vs MoE), and how to match renderers to models. Use when the user asks which model to use, what models are available, or how to pick a model for their task.

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。

Guide for installing Tinker, setting up the environment, getting an API key, and verifying everything works. Use when the user is getting started, setting up their environment, or troubleshooting installation issues.

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 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.

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。