real

Models

Purpose

model train/status/plan, LoRA methods, trainer contract, and ModelScope inspect/diff/probe/decompile/patch.

model train real
model status real
model inspect real
model diff real
model probe real
model decompile experimental
model patch experimental
model eval real
model stage real

Example

# VoiceCore reference — Sam store persona LoRA (redacted for public)
# Dataset: #321 mini-freeze 140 rows
# sha256: 272b64eb1d40aaf8a186df6a2f6c8e7090a1d69d03ac4928ac75a246f8e439f0
# Live trainer: SPARK_TRAIN_URL (host redacted)
# Histogram floors of 20 fail on a thin freeze — that is the gate.
model code
model train dataset "examples/fixtures/train/dataset.jsonl" base "hf://Qwen/Qwen3-27B" out "out/train/job-lora-001" backend "http" method "lora_bf16" -> job
model status "job-lora-001" -> status
expect gte status $.metrics.golden_bucket 0.8
expect eq status $.metrics.grounding_facts true
expect eq status $.metrics.rachel_bleed 0
expect histogram_min status W2a 20
expect histogram_min status W2b 20
expect histogram_min status W-SNC 20
expect histogram_min status W-TF-RECOVER 20

Dry-run vs live

Mode	Behavior
dry-run	Fixtures only. No GPU. No network.
live	Needs keys / trainer URL. Fail-loud on miss.

Exit codes

Exit	Meaning
0	success / expect pass
1	expect fail or language fail
2	usage / unknown method / missing program
4	credential unavailable or CAP miss

What it does not do

Does not load staged adapters into serving without an owner token.