Launch and manage LLM post-training runs with Tinker backend.
npx skills add https://github.com/ar0cket1/hermes-research-agent --skill tinker-ar0cket1
Please help me install this Agent Skill. Skill: tinker Source: https://github.com/ar0cket1/hermes-research-agent/tree/main/skills/mlops/training/tinker Command: npx skills add https://github.com/ar0cket1/hermes-research-agent --skill tinker-ar0cket1
Tinker coordinates and runs post-training workflows for LLMs, enabling CPU-side orchestration with remote GPU execution to streamline SFT, DPO, and PPO-style experiments.
Start a post-training run with tinker_posttrain(action='start_run', ...) and monitor progress using research_loop(action='monitor_run', ...).
High-intent search queries and answers about installing and using this skill.
LLM post-training orchestration across CPU and GPU resources is managed by launching runs with tinker_posttrain and monitoring them via research_loop. This coordinates SFT, DPO, and PPO workflows using remote GPU execution for streamlined experiments.
Yes, long-running PPO fine-tuning jobs can be resumed from periodic checkpoints. The backend lifecycle management handles checkpointing and resumption automatically, ensuring progress is saved during remote GPU execution and restored upon restart.
SFT and DPO training runs support prompt/completion data formats, while PPO workflows accept prompts alongside token-level logs. This standardized data format compatibility ensures proper lifecycle management across different post-training experiments.
Yes, you need a TINKER_API_KEY to manage post-training lifecycle and monitoring. This key authenticates the CPU-side orchestration commands that launch remote GPU execution for SFT, DPO, and PPO workflows.
The best way to monitor an active fine-tuning run is by using the research_loop component with the monitor_run action. This tracks the progress of post-training workflows executing on remote GPUs and maintains standardized run hygiene.
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