Guide for the Tinker CLI — managing training runs, checkpoints, downloading weights, and publishing to HuggingFace. Use when the user asks about CLI commands, listing runs, managing checkpoints from the terminal, or uploading to HF Hub.
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安装前可以查看交给 AI 代理的指令内容。
The tinker CLI is installed with the Tinker Python SDK. It provides commands for managing training runs and checkpoints from the terminal.
Requires TINKER_API_KEY environment variable to be set.
tinker --format table # Rich table output (default)
tinker --format json # JSON output (for scripting)
# List recent training runs
tinker run list
tinker run list --limit 50
# Show details for a specific run
tinker run info <RUN_ID>
# Custom columns
tinker run list --columns id,model,lora,updated,status,checkpoint
Available columns: id, model, owner, lora, updated, status, checkpoint, checkpoint_time.
# List checkpoints for a specific run
tinker checkpoint list --run-id <RUN_ID>
# List all your checkpoints across runs
tinker checkpoint list
tinker checkpoint list --limit 50
# Show checkpoint details
tinker checkpoint info <TINKER_PATH>
# Download and extract a checkpoint
tinker checkpoint download <TINKER_PATH>
tinker checkpoint download <TINKER_PATH> --output ./my-adapter
tinker checkpoint download <TINKER_PATH> --force # Overwrite existing
# Make a checkpoint publicly accessible
tinker checkpoint publish <TINKER_PATH>
# Make a checkpoint private
tinker checkpoint unpublish <TINKER_PATH>
# Set checkpoint to expire in 24 hours
tinker checkpoint set-ttl <TINKER_PATH> --ttl 86400
# Remove expiration (keep indefinitely)
tinker checkpoint set-ttl <TINKER_PATH> --remove
# Delete checkpoints (with confirmation prompt)
tinker checkpoint delete <TINKER_PATH>
# Delete without confirmation
tinker checkpoint delete <TINKER_PATH> -y
# Delete multiple
tinker checkpoint delete <PATH1> <PATH2> <PATH3>
# Push checkpoint to HuggingFace
tinker checkpoint push-hf <TINKER_PATH> --repo user/my-model
# Push as public repo
tinker checkpoint push-hf <TINKER_PATH> --repo user/my-model --public
# Advanced options
tinker checkpoint push-hf <TINKER_PATH> \
--repo user/my-model \
--revision main \
--commit-message "Upload fine-tuned model" \
--create-pr \
--no-model-card
Options: --repo, --public, --revision, --commit-message, --create-pr, --allow-pattern, --ignore-pattern, --no-model-card.
tinker version # e.g. "tinker 0.15.0"
# Get checkpoint paths as JSON for scripting
tinker checkpoint list --format json | jq '.[].tinker_path'
# Get run IDs
tinker run list --format json | jq '.[].id'
# 1. Find your training run
tinker run list
# 2. List checkpoints for that run
tinker checkpoint list --run-id <RUN_ID>
# 3. Download the final checkpoint
tinker checkpoint download tinker://<RUN_ID>/sampler_weights/final -o ./adapter
# 4. Or push directly to HuggingFace
tinker checkpoint push-hf tinker://<RUN_ID>/sampler_weights/final --repo user/my-model
TINKER_API_KEY must be set — the CLI reads it from the environmenttinker://<run-id>/<type>/<checkpoint-id>push-hf uploads the raw checkpoint — for merged HF models, use weights.build_hf_model() in Python first (see /weights skill)delete is permanent and irreversible — use -y flag carefully还没有评价。欢迎分享你的使用感受。
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Guide for checkpointing — saving, loading, and resuming training with CheckpointRecord. Use when the user asks about saving weights, resuming training, checkpoint management, or the checkpoint lifecycle.
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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.
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Guide for using completers — TokenCompleter and MessageCompleter for text generation during RL rollouts and evaluation. Use when the user asks about generating text, completing messages, or using completers in RL environments.
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Guide for contributing to the tinker-cookbook repo — development setup, code style, type checking, PR process, and design conventions. Use when the user asks about how to contribute, set up the dev environment, code style, or project conventions.
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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.
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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.
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