Skip to main content
rlcli provides two ways to interact with trained models: rlcli sample for quick generation from base models or checkpoints, and passthrough commands (checkpoint, run, session) that delegate to the official tinker CLI against your local server.

Sampling

Use rlcli sample to generate text from a base model or a fine-tuned checkpoint. Sampling uses raw generation with no chat template applied.

Sample from a base model

Sample from a checkpoint

Common options

rlcli uses the model’s tokenizer for encode and decode. You can override the tokenizer model name with the RLCLI_TOKENIZER environment variable.

Managing checkpoints and runs

rlcli provides thin passthrough commands to the official tinker CLI. These set TINKER_BASE_URL to your local server and TINKER_API_KEY=tml-dummy if unset, then invoke python -m tinker.cli <command>.

List checkpoints

Inspect runs

Session management

These commands accept the same flags and arguments as the upstream tinker CLI. For full details on available subcommands and options, consult the official Tinker documentation at tinker-docs.thinkingmachines.ai.
The passthrough commands are rlcli checkpoint, rlcli run, and rlcli session. They are not native rlcli implementations but are pre-configured to talk to your local SkyRL server.

Next steps