finetune-model

# Check fine-tuning readiness This skill checks preparation only. It provides **no training recipe**. A `ready` result always has `training_validated: false`. Multi-node training is unsupported here; do not invent a distributed command or report a successful fine-tune from this check. ## Preconditions - Read the [readiness guide](../../docs/model-workflows/finetune-readiness.md). Run from the repository root as the intended model consumer on the selected compute host, not the login node. The validator checks local host identity against the scheduler record; do not bypass a mismatch. - Obtain authorization for the target and read-only scheduler queries. Specify one node and one partition. An IDLE node with an unallocated GPU is required; this conservative check does not accept mixed, draining or flagged nodes. - Follow [manage-model-cache](../manage-model-cache/SKILL.md) for an approved shared cache, immutable revision and complete reviewed required-file list. Supported inputs are Hugging Face `tokenizer.json` plus safetensors weights; list every extra model-specific asset. Indexed weights require every shard. - Select a site-reviewed fine-tuning image pinned by digest, using Pyxis syntax `registry#repository@sha256:<digest>`. Do not use mutable tags, credentials in the reference, or substitute an unreviewed image to get a pass. ## Readiness check Set the variables to reviewed values. For sharded weights, replace the final file argument with `model.safetensors.index.json`; the validator follows its weight map. Add every additional file needed by the selected model. ```bash python3 scripts/validation/validate_finetune.py --json \ --node "$NODE" --partition "$PARTITION" \ --container-image "$TRAINING_IMAGE" \ --cache-dir "$HF_HUB_CACHE" --repo-id "$MODEL_REPO" --revision "$MODEL_REVISION" \ --require-file config.json --require-file tokenizer.json \ --require-file tokenizer_config.json --require-file model.safetensors ``` | Result | Next action | |---|---| | Exit `0`, `status: ready`, `ok: true` | Report readiness only, including that container execution and training remain unproven. | | Exit `1`, `status: not_ready` | Report every `errors[]` stage. Repair the named prerequisite through its approved procedure; do not drain nodes, install software or download models implicitly. | | Exit `2`, `status: bad_input` | Correct explicit inputs or unsupported flags using `--help`. No scheduler commands run for bad input. | ## Optional one-GPU probe Off by default. Only with explicit authorization to allocate a GPU and run the reviewed image, repeat the command with `--gpu-smoke`. This can download the container image, but its scheduler window is only five seconds, so pre-pull the pinned image on the node first. It submits one bounded, single-node, single-GPU `nvidia-smi` job after all readiness gates pass. It does not load the model or train. `gpu_smoke_ok: true` proves only the container GPU probe, not cache access inside the container, framework compatibility, learning or checkpoint recovery. Keep credentials private, mount shared inputs read-only in any future reviewed recipe, and keep outputs/checkpoints outside the input cache. A readable cache, a pinned image string, and a GPU probe cannot certify a training outcome.