> ## Documentation Index > Fetch the complete documentation index at: https://docs.trymonte.ai/llms.txt > Use this file to discover all available pages before exploring further. # Runs, lineages, and the ledger > How executions are recorded, chained, and read Each training or evaluation is a Run. Every Run originates in exactly one Experiment and is a row in its Measurement's Ledger. This page covers the row's whole life: launch, record, chain, read. ## Runs A Run is one of two kinds. A training Run (`train@N`) produces a Checkpoint. An eval Run (`eval@N`) scores one, with `eval@0` scoring the base model. **N is the Checkpoint's absolute step count, never a run index.** The training Run that ends at step 50 is `train@50`. `eval@50` scores that Checkpoint. Address a Run as `/` anywhere the CLI takes one (`logs`, `show`, `stop`, `promote`). Use the run ID or an alias like `eval@0`. When an alias matches several rows, the latest wins. ## Checkpoints A Checkpoint is the model's saved state at one step. Two shapes matter. The full resume set (weights, optimizer, and dataloader state) continues training. The servable folder (an HF-layout weights and tokenizer directory) is what an inference server loads. A load-check proves a servable folder actually serves: one prompt through a real serving stack. It is not a score and not a Run. The final claim's Checkpoint selection is loop discipline: see [The loop](/concepts/loop). ## Lineages A Lineage is the group of Runs sharing a base model and comparability config. You read deltas within a Lineage, never across two. Each Lineage needs its own Baseline. Monte derives a Lineage from row fields at read time and never stores it. ## The Ledger The Ledger is append-only: rows are never rewritten. Each Run writes its own event file, one writer per file. Every surface (the CLI, the Console) reads through `monte.api`, never the files. The file format is internal. Every row records full provenance: git SHA, config hash, Split hash, eval-config hash, comparability hash, cost, and an image digest. A training Run's digest is the true container image digest. An eval Run serves from a venv, so its digest is a `venv:` hash over the serving stack's resolved versions. Any score is traceable to its exact inputs. ## Traces A Trace is one Run's attempt at one Task: the rollout verbatim plus the grader's fields (score, expected and extracted answer). Per-task results and Traces have no CLI command. Read them in the [Console](/console/overview), or through `monte.api` from Python. ## One command at a time One lock per Measurement serializes mutating commands. A second command refuses instead of waiting. A dead process on the same host reclaims automatically. If a gone box left a lock behind, you remove it by hand: see [Troubleshooting](/troubleshooting). ## Run artifacts Each run directory on the durable tier holds the launched plan (`plan.json`), the event file (`events.jsonl`), and for training Runs the training log and tensorboard files. ## Related pages What the rows mean: baselines, deltas, and the final claim. The commands that write and read these rows.