nemo-customizer

name	nemo-customizer
description	Fine-tune models on NeMo Platform with `automodel`, `unsloth`, or `rl` (all `submit`-only): HF dataset conversion, filesets, model entities, and job JSON (hyperparameters, batch, schedule, optimizer) + job polling. `automodel`/`unsloth` run SFT/LoRA as Docker GPU jobs; `rl` runs DPO (preference optimization) on a Ray cluster (Kubernetes). Use for train, fine-tune, customize, SFT, LoRA, DPO, preference optimization, learning rate, epochs, or nemo customization.
triggers	["nemo-customizer","nemo customizer","fine-tune","fine tune","finetune","train a model","customize a model","sft","lora","dpo","direct preference optimization","preference optimization","preference tuning","automodel","unsloth","nemo-rl","nemo rl","nemo customization","nemo-customization","customizer","customization training","automodel submit","unsloth submit","rl submit"]
not-for	["nemo-build-agent (agent scaffold/deploy, not weight training)","nemo-explore (agent design only)","nemo-setup (platform install; route here when CLI resolution fails)","safe-synthesizer (tabular synthetic data training)"]
compatibility	Requires nemo-customizer-plugin and a customization contributor (`nemo.customization.contributors`). Platform must expose jobs, files, and models APIs.
maturity	active
license	Apache-2.0
user-invocable	true
allowed-tools	["Bash","Read","Grep"]

NeMo Customizer

End-to-end SFT + LoRA (automodel/unsloth) and DPO (rl) on NeMo Platform. Three backend plugins ship in this repo — all are submit-only (local run is hard-disabled on each):

Backend	Verb	Trains	Where it runs	Pick when
automodel (default)	submit	SFT / LoRA	Platform Docker GPU executor (Jobs service schedules containers on the platform host's daemon)	General SFT/LoRA; multi-GPU (data/tensor parallel); distillation; full-weight SFT
unsloth	submit	SFT / LoRA	Same — Docker GPU job with 4 steps (download → train → upload → model-entity)	User asks for Unsloth, or wants Unsloth's 4-bit LoRA path / optimizer defaults on a single GPU
rl	submit	DPO (preference)	Platform Kubernetes executor — provisions a Ray cluster; 4 steps (download → DPO train → upload → model-entity)	Preference optimization / DPO / RLHF-style alignment from a {prompt, chosen, rejected} dataset; full-weight only

nemo-customizer is the router (nemo customization …); training backends are separate plugins (nemo-automodel, nemo-unsloth, nemo-rl). submit posts to the platform API; the platform runs training in container steps — not in the CLI shell. Heavy ML deps live in container images only.

Runtime split: automodel/unsloth need platform.runtime: docker; rl needs platform.runtime: kubernetes (no local Docker fallback — it schedules a Ray cluster on the remote cluster). A given platform is usually one or the other — confirm with execution profiles before picking rl.

Decision rule below in Plugin pick. Batch shell work; reuse resources with --exist-ok; skip CLI --help unless a command fails.

Pre-flight — CLI resolution

Run from the nemo-platform git root (top-level pyproject.toml), not a plugin subfolder. Example commands below use nemo … — resolve the invocation once before any other step:

cd /path/to/nemo-platform
if command -v nemo >/dev/null 2>&1; then
  echo "nemo"
elif command -v uv >/dev/null 2>&1 && uv run nemo --help >/dev/null 2>&1; then
  echo "uv run nemo"
else
  echo "CLI_NOT_FOUND"
fi

Result	Action
nemo	Use nemo … for all commands in this workflow
uv run nemo	Prefix every command with uv run (repo dev checkout without nemo on PATH)
CLI_NOT_FOUND	Stop. Route to nemo-setup (make bootstrap then nemo setup from the nemo-platform repo root). Do not continue.

Authentication (optional)

Platform auth is not required to run customization when the cluster has authentication disabled. Check with nemo auth status — if it reports authentication is disabled, skip login and proceed.

When auth is enabled on the connected platform, API calls need credentials:

Situation	Action
Auth disabled	Skip login
Auth enabled, unsigned JWT allowed (typical local dev: auth.allow_unsigned_jwt: true)	nemo auth login --unsigned-token --email <user email or [e-mail]>
Auth enabled, OIDC configured	nemo auth login (or --username / --password for non-interactive)
401/403 on any platform call	Run the matching login above, then retry

Use [e-mail] unless the user specifies another email. Run nemo auth status after login to confirm.

HuggingFace token (gated models)

Gated HF repos (Llama, Gemma, Mistral instruct, …) need a platform secret (convention: hf-token) referenced as token_secret on the model fileset — not in job JSON (unlike W&B's api_key_secret). The Files service does not read your local ~/.cache/huggingface or shell HF_TOKEN.

Model access	Action
Public (e.g. Qwen/Qwen3-1.7B)	Skip; omit token_secret on the fileset
Gated / private HF repo	Before model fileset creation or job submit: nemo secrets list --workspace default and confirm hf-token exists. If missing, ask the user for their HF token and stop — do not create the fileset or submit until wired up.

Full create/update commands, fileset token_secret, license acceptance, and download-phase errors: references/troubleshooting.md § Gated HuggingFace models.

Plugin pick

1. Run nemo jobs list-execution-profiles -f json (login first only if auth is enabled — see Authentication; see references/troubleshooting.md for parsing).
2. If the task is DPO / preference optimization (a {prompt, chosen, rejected} dataset, "align", "preference", "RLHF-style") or the user explicitly asked for NeMo-RL → rl (requires a GPU profile and platform.runtime: kubernetes).
3. Else if the user explicitly asked for Unsloth → unsloth.
4. Else if the user explicitly asked for Automodel → automodel.
5. Else if any profile has provider: gpu or gpu_distributed → automodel (default, SFT/LoRA).
6. Else stop and tell the user GPU customization is unavailable (all backends need a GPU execution profile; automodel/unsloth also need platform.runtime: docker, rl needs platform.runtime: kubernetes).

rl runtime gate: rl submit fails fast unless the platform runs platform.runtime: kubernetes (require_distributed_runtime). rl job steps execute as Kubernetes pods via the kubernetes_job execution backend — the docker job backend cannot run rl. Before submitting rl, confirm with nemo jobs list-execution-profiles -f json that the cpu/gpu profiles report backend: kubernetes_job (or volcano_job). If they report backend: docker/subprocess, the platform is not configured for rl: stop and tell the user DPO needs a Kubernetes-runtime platform — do not start/reuse a docker-runtime platform, and do not fall back to automodel/unsloth (those are SFT/LoRA, not DPO). To stand up or configure one, see references/rl-kubernetes-runtime.md.

For automodel/unsloth, training never runs inside the nemo CLI process. After submit, the platform's local Docker executor launches GPU container steps on the daemon attached to that platform host (often the same machine as http://127.0.0.1:8080, but always query the platform — not the agent's shell GPU or a separate docker info on another box). rl does not use the Docker executor — its steps run on the Kubernetes cluster the platform is configured against.

Gotchas

• Resolve the CLI per Pre-flight — CLI resolution before any nemo … command; run from the nemo-platform git root, not a plugin subfolder.
• Set NMP_BASE_URL only when the user gives a platform URL; default http://127.0.0.1:8080 (same as http://localhost:8080). The nemo CLI reads this env var (see SDK NMP_BASE_URL). Track whether the user overrode the base URL — see Platform unreachable below.
• Platform unreachable — if any platform API call fails with a connection error (Connection error, timeout, refused):

  • User gave a custom URL or you exported a non-default NMP_BASE_URL: stop and tell the user the platform is not reachable at that address. Do not offer to start local services.

  • Default URL only (no user override): ask whether to start the platform locally. If they agree, from the nemo-platform git root run in the background:

    nemo services run \
      --host 0.0.0.0 \
      --port 8080 \
      --controllers jobs,entities,models \
      --service-group all
    

    Poll until healthy (curl -sf http://127.0.0.1:8080/health/ready or retry nemo jobs list-execution-profiles -f json), then continue the workflow. Do not start services without asking.

    • ⚠️ This default start is a DOCKER-runtime platform — valid for automodel/unsloth only. It is NOT valid for rl: rl needs platform.runtime: kubernetes with a kubernetes_job execution backend. Starting this default and submitting rl will fail the runtime gate. For rl, configure/point at a Kubernetes-runtime platform instead — see references/rl-kubernetes-runtime.md. Never start or reuse a docker-runtime platform for rl.
• All backends are submit only — nemo customization <plugin> run … hard-fails with a pointer to submit (automodel, unsloth, and rl each disable local run). Do not improvise verbs or pass --venv.
• Never set max_steps together with epochs (automodel + unsloth; rl has the same caveat — see rl (DPO) gotchas). max_steps is a global cap and stops mid-epoch. Test fixtures include max_steps for smoke tests — do not copy into production jobs. Unsloth's schema enforces this as a hard mutex; automodel allows both but the result is surprising.
• Job done (all backends) = top-level status in completed | error | cancelled. Steps can all be completed while the job is still active (upload, entity registration). status_details.phase may stay training with progress_pct: 100 for a long time — keep polling. poll_customization_job.sh works for any job id (automodel-…, unsloth-…, or rl-…); it exits 1 on error or cancelled.
• Model spec fills async: submit without polling nemo models get unless submit fails.
• HF dataset id from the user → convert locally; do not ask for local paths first.
• Dataset fileset name = HF dataset name only (tau/commonsense_qa → commonsense_qa), not the model name.
• Prefer CHAT JSONL when the model has a chat template; details in references/dataset-formats.md (automodel auto-detects schema; unsloth needs dataset.apply_chat_template: true to consume messages).
• User asks to tune batch or parallelism (automodel) → references/batch-sizing.md. Other fields (LR, epochs, LoRA rank, distillation) → references/hyperparameters-automodel.md. For unsloth batch sizing see references/batch-sizing.md; for unsloth fields see references/hyperparameters-unsloth.md. Run nemo customization <plugin> explain for the live schema.
• Skill defaults (micro_batch_size 1, global_batch_size 4) are safe on unknown VRAM. When the user has ≥48 GB on one GPU, use references/batch-sizing.md instead of defaults. Unsloth's analogues are batch.per_device_train_batch_size and batch.gradient_accumulation_steps (effective batch = product).
• Unsloth training is single-GPU per job (inside the container). hardware.gpus sets CUDA_VISIBLE_DEVICES before import torch — selection, not reservation. No parallelism/TP/PP block in job JSON. Multi-GPU sharding → use automodel. Pass --profile <name> on unsloth submit when the default gpu profile is wrong (automodel sets training.execution_profile in JSON instead).
• Unsloth validation defaults — when dataset.validation_path is set and schedule.eval_steps is omitted, the trainer runs validation once per effective epoch automatically. Report final metrics.val_loss from job status (see references/reporting.md). Set eval_steps explicitly to override cadence.
• Do not use local docker info to pick automodel vs unsloth. Run nemo jobs list-execution-profiles -f json against the user's platform (login first only if auth is enabled — see Authentication; see references/troubleshooting.md). Default output is a table — -f json is required for scripting; parse stdout only (do not pipe 2>&1 into json.load).
• Do not merge stderr into stdout when parsing JSON — submit, explain, and -f json commands write JSON on stdout; harmless warnings like Configuration file not found, using defaults go to stderr. Piping with 2>&1 before json.load raises JSONDecodeError even when submit succeeded — a common cause of duplicate jobs when the agent re-submits after a parse error. Parse stdout only; redirect stderr if needed (2>/dev/null). See references/troubleshooting.md § Parsing CLI JSON.
• For submit/image/plugin errors (all backends), read references/troubleshooting.md. Unsloth needs the nmp-unsloth-training container image on the platform host's Docker daemon (see docker/unsloth/README.md); rl needs the nmp-rl-tasks / nmp-rl-training images on the Kubernetes cluster (see rl (DPO) gotchas and references/rl-kubernetes-runtime.md).
• Missing training image on a remote platform — if the user gave a non-localhost NMP_BASE_URL and the job errors with Failed to pull image, manifest unknown, or missing nmp-unsloth-training / automodel training image: do not run docker build, docker pull, or docker buildx bake on the agent machine. Report with the template in references/reporting.md (use Output adapter fileset (planned): on error), then append on-target build steps from references/troubleshooting.md § Missing training images.
• Gated HuggingFace models (Llama, Gemma, …) — confirm hf-token + fileset token_secret before submit; download fails with Failed to access upstream storage / 502 when missing. See HuggingFace token (gated models) and references/troubleshooting.md § Gated HuggingFace models.
• Post-training eval format — use the same CHAT messages JSONL as training. Do not flatten rows to prompt/expected for the evaluator. Send messages[:-1] at inference (exclude final assistant label); score against messages[-1].content. See references/post-training-eval.md and references/eval_helpers.py.
• LoRA adapters load automatically for eval — when a LoRA job completes (save_method: lora), the adapter is registered on the base model entity and hot-reloaded on any READY deployment with lora_enabled: true. Do not create or update deployments before LoRA eval. Full SFT (finetuning_type: all_weights) and merged checkpoints (merged_16bit / merged_4bit) register a new model entity at output.name — deploy that entity for inference before chat or eval; full weights are not hot-reloaded onto the base deployment. For LoRA eval, route through the provider gateway (/provider/<name>/-/v1 with model: default--<adapter>); the model-entity path (/model/<entity>/-/v1) always hits the base model. See references/post-training-eval.md § Request routing (base vs LoRA).

rl (DPO) gotchas

• rl is DPO, not SFT — it trains on preference pairs {prompt, chosen, rejected}, full-weight (no LoRA/adapter; finetuning_type is not user-set). Don't route SFT/LoRA work here, and don't route DPO to automodel/unsloth.
• One preference fileset, two files — dataset is a single string ref to a fileset that holds both training.jsonl and validation.jsonl (uploaded with --remote-path). Unlike automodel (dataset.training/dataset.validation) and unsloth (dataset.path/validation_path), there is no separate validation ref. See references/dataset-formats.md § NeMo-RL.
• String refs — model and dataset are plain strings ("workspace/name"), not objects. The training method goes under training with type: "dpo".
• Kubernetes job backend, not Docker — rl steps run as Kubernetes pods via the kubernetes_job backend; the docker job backend cannot run rl. rl submit fails fast on a docker-runtime platform. The target cluster must have the job-step images (nmp-rl-tasks, nmp-rl-training), the jobs-launcher image (the per-step init container), and a job-storage PVC. Verify the platform with nemo jobs list-execution-profiles -f json (expect backend: kubernetes_job); to configure one, see references/rl-kubernetes-runtime.md. Multi-node (parallelism.num_nodes > 1) also needs the platform-side NMP_RL_MULTINODE_SHARED_STORAGE_PATH (shared FS for Ray coordination) or compile fails fast.
• Job id prefix is rl-<hex> and the platform auto-generates it — rl submit has no --name flag (the job JSON name is the output name, not the job id). Derive the job id from nemo jobs list (newest rl-*) for polling; poll_customization_job.sh rl-<id> works.
• DPO main knob is ref_policy_kl_penalty (β). For OOM, enable activation_checkpointing: true first. Full DPO field reference: references/hyperparameters-rl.md.
• max_steps + epochs — same caveat as the other backends: max_steps caps mid-epoch; it's in the smoke fixture (plugins/nemo-rl/tests/fixtures/minimal_dpo.json) — omit for real runs.

Workflow

Common steps then branch by plugin pick:

- [ ] Resolve CLI (Pre-flight — CLI resolution); cd nemo-platform
- [ ] export NMP_BASE_URL (if user provided endpoint); note whether base URL is user-overridden
- [ ] nemo auth status — skip login if auth disabled; if auth enabled and unsigned JWT allowed, `nemo auth login --unsigned-token --email <…>`; if OIDC, `nemo auth login`
- [ ] nemo jobs list-execution-profiles -f json — apply Plugin pick rules above (retry login on 401/403)
- [ ] On connection error: default URL → ask to start platform (see Platform unreachable); custom URL → report unreachable and stop
- [ ] Convert HF dataset → /tmp/train-data/*.jsonl (see references/hf-conversion.md)
- [ ] Create dataset fileset (--exist-ok), upload train.jsonl (+ validation.jsonl), nemo files list to verify
- [ ] Gated HF base model? → confirm `hf-token` exists; ask user and stop if missing (see HuggingFace token + troubleshooting § Gated HuggingFace models)
- [ ] Create HF weights fileset + model entity if missing (--exist-ok; gated repos need `token_secret` on fileset — see troubleshooting)

# automodel branch (submit → Docker GPU job)
- [ ] Write /tmp/job.json (batch sizing for ≥48 GB GPU; else Defaults table)
- [ ] nemo customization automodel submit /tmp/job.json --workspace default
- [ ] Poll until top-level terminal (`poll_customization_job.sh`; default 15s interval, or 30–60s manual polls)
- [ ] Report using the template in `references/reporting.md`
- [ ] Optional: compare base vs adapter on validation — `references/eval_helpers.py …` (LoRA only; CHAT format; adapters hot-reload automatically; see `references/post-training-eval.md`)

# unsloth branch (submit → Docker GPU job)
- [ ] Write /tmp/job.json using the UnslothJobInput shape (see Fast path — unsloth)
- [ ] nemo customization unsloth submit /tmp/job.json --workspace default [--profile <gpu-profile>]
- [ ] Poll until top-level terminal (`poll_customization_job.sh unsloth-<job-id>`; default 15s interval)
- [ ] Report using the template in `references/reporting.md`
- [ ] Optional: compare base vs adapter on validation — `references/eval_helpers.py …` (LoRA only; CHAT format; adapters hot-reload automatically; see `references/post-training-eval.md`)

# rl branch (DPO; submit → Kubernetes/Ray job) — requires platform.runtime: kubernetes
- [ ] Verify execution backend: `nemo jobs list-execution-profiles -f json` shows cpu/gpu at `backend: kubernetes_job` (NOT docker/subprocess). If not → stop; do not start a docker platform; configure per references/rl-kubernetes-runtime.md
- [ ] Dataset is PREFERENCE data: upload training.jsonl + validation.jsonl ({prompt,chosen,rejected}) to ONE fileset
- [ ] Write /tmp/job.json using the RlJobInput shape (see Fast path — rl (DPO))
- [ ] nemo customization rl submit /tmp/job.json --workspace default [--profile <gpu-profile>]
- [ ] Derive job id (newest rl-* from `nemo jobs list` — submit has no --name flag)
- [ ] Poll until top-level terminal (`poll_customization_job.sh rl-<job-id>`; default 15s interval)
- [ ] Report using the template in `references/reporting.md`

Fast path — automodel

Substitute <hf-repo>, <hf-dataset>, <model-entity>, <weights-fileset>, <dataset-fileset>, <output-name>.

Setup

export NMP_BASE_URL=http://127.0.0.1:8080   # user override only
cd /path/to/nemo-platform
nemo auth status   # skip login if auth disabled; if enabled + unsigned JWT allowed → login --unsigned-token --email [e-mail]
nemo jobs list-execution-profiles -f json   # platform GPU profiles → automodel; set training.execution_profile if needed

1. Dataset — convert per references/hf-conversion.md, then:

DATASET=<dataset-fileset>   # e.g. commonsense_qa
nemo files filesets create "$DATASET" --workspace default --purpose dataset --exist-ok
nemo files upload /tmp/train-data/train.jsonl "$DATASET" --workspace default --remote-path train.jsonl
# validation.jsonl if present
nemo files list "$DATASET" --workspace default

2. Model — skip if entity exists (nemo models list --workspace default). For gated HF repos, complete HuggingFace token (gated models) first — see references/troubleshooting.md § Gated HuggingFace models for token_secret on the fileset.

WEIGHTS=<weights-fileset>   # e.g. qwen3-1.7b
MODEL_ENTITY=<model-entity>   # Models API entity (not dataset fileset, not HF id)
HF_REPO=<hf-repo>           # e.g. Qwen/Qwen3-1.7B

nemo files filesets create "$WEIGHTS" --workspace default --purpose model --exist-ok \
  --storage '{"type":"huggingface","repo_id":"'"$HF_REPO"'","repo_type":"model","revision":"main"}'

nemo models create "$MODEL_ENTITY" --workspace default --exist-ok \
  --input-data '{"name":"'"$MODEL_ENTITY"'","fileset":"default/'"$WEIGHTS"'","custom_fields":{"hf_model_id":"'"$HF_REPO"'"}}'

For gated repos, add "token_secret":"hf-token" to the --storage JSON (after creating the secret). See troubleshooting § Gated HuggingFace models.

3. Job JSON — write /tmp/job.json. model is the registered model entity (default/<model-entity>), not an HF repo id or dataset fileset. Full hyperparameter reference: references/hyperparameters-automodel.md.

{
  "model": "default/<model-entity>",
  "dataset": {
    "training": "default/<dataset-fileset>",
    "validation": "default/<dataset-fileset>"
  },
  "training": {
    "training_type": "sft",
    "finetuning_type": "lora",
    "lora": { "rank": 16, "alpha": 32 },
    "max_seq_length": 2048
  },
  "schedule": { "epochs": 1 },
  "batch": { "global_batch_size": 4, "micro_batch_size": 1 },
  "optimizer": { "learning_rate": 5e-5, "weight_decay": 0.01, "warmup_steps": 0 },
  "parallelism": { "num_nodes": 1, "num_gpus_per_node": 1, "tensor_parallel_size": 1 },
  "output": { "name": "<output-name>" }
}

4. Submit and poll

nemo customization automodel submit /tmp/job.json --workspace default
bash plugins/nemo-customizer/src/nemo_customizer/skills/nemo-customizer/scripts/poll_customization_job.sh automodel-<job-id>

Read <job-id> from the "name" field in submit stdout (JSON). Do not use 2>&1 before json.load — warnings on stderr break parsing; see Gotchas. Optional interval override: append seconds (e.g. … 30). Or poll manually: nemo jobs get-status automodel-<job-id> every 30–60s.

Fast path — unsloth

Same substitutions as automodel. Steps 1 (dataset) and 2 (model entity) are identical — the differences are the job JSON shape (UnslothJobInput) and the unsloth submit command.

1. Dataset — same as automodel Fast path step 1.

2. Model — same as automodel Fast path step 2.

3. Job JSON — write /tmp/job.json using the UnslothJobInput shape (see references/hyperparameters-unsloth.md). model is an object (not a string), dataset.path is a single fileset ref, hardware.gpus replaces the parallelism block (single GPU in the training container). nemo customization unsloth explain prints the live schema.

{
  "name": "<job-name>",
  "model": {
    "name": "default/<model-entity>",
    "max_seq_length": 2048,
    "load_in_4bit": true,
    "dtype": "auto"
  },
  "dataset": {
    "path": "default/<dataset-fileset>",
    "text_field": "text",
    "apply_chat_template": true
  },
  "training": {
    "training_type": "sft",
    "finetuning_type": "lora",
    "lora": { "rank": 16, "alpha": 32 }
  },
  "schedule": { "epochs": 1, "warmup_ratio": 0.1 },
  "batch": { "per_device_train_batch_size": 2, "gradient_accumulation_steps": 4 },
  "optimizer": { "learning_rate": 5e-5, "optim": "adamw_8bit" },
  "hardware": { "gpus": "0", "precision": "bf16" },
  "output": { "name": "<output-name>", "save_method": "lora" }
}

If the model uses messages chat format (preferred when the tokenizer has a chat template), keep dataset.apply_chat_template: true. Otherwise emit a single text column from your converter and set apply_chat_template: false.

4. Submit and poll

nemo customization unsloth submit /tmp/job.json --workspace default
bash plugins/nemo-customizer/src/nemo_customizer/skills/nemo-customizer/scripts/poll_customization_job.sh unsloth-<job-id>

Read <job-id> from the "name" field in submit stdout (JSON). Do not use 2>&1 before json.load — warnings on stderr break parsing; see Gotchas. Optional interval override: append seconds (e.g. … 30). Or poll manually: nemo jobs get-status unsloth-<job-id> every 30–60s. If submit fails on an unknown profile, re-list execution profiles and pass --profile <name> on submit (default is gpu).

If you try nemo customization unsloth run …, the CLI hard-fails with a pointer to submit.

Fast path — rl (DPO)

DPO on a Ray cluster — Kubernetes runtime only, full-weight. Before anything else, confirm the platform dispatches jobs to Kubernetes: nemo jobs list-execution-profiles -f json must show cpu/gpu at backend: kubernetes_job (not docker/subprocess). If it doesn't, stop — do not start/use a docker-runtime platform; configure a Kubernetes-runtime one per references/rl-kubernetes-runtime.md. Model-entity setup (step 2) is identical to automodel; the dataset is preference data and the job JSON is the RlJobInput shape.

1. Preference dataset — rows are {prompt, chosen, rejected} (see references/dataset-formats.md § NeMo-RL). Upload both files to one fileset:

DATASET=<preference-fileset>   # e.g. dpo-data
nemo files filesets create "$DATASET" --workspace default --purpose dataset --exist-ok
nemo files upload /tmp/dpo-train.jsonl "$DATASET" --workspace default --remote-path training.jsonl
nemo files upload /tmp/dpo-val.jsonl   "$DATASET" --workspace default --remote-path validation.jsonl
nemo files list "$DATASET" --workspace default

2. Model — same as automodel Fast path step 2 (HF weights fileset + model entity; gated repos need token_secret).

3. Job JSON — write /tmp/job.json. model and dataset are strings; the method is under training with type: "dpo". Full field reference: references/hyperparameters-rl.md.

{
  "model": "default/<model-entity>",
  "dataset": "default/<preference-fileset>",
  "training": {
    "type": "dpo",
    "epochs": 1,
    "learning_rate": 5e-6,
    "max_seq_length": 1024,
    "batch_size": 32,
    "micro_batch_size": 1,
    "ref_policy_kl_penalty": 0.05,
    "parallelism": { "num_nodes": 1, "num_gpus_per_node": 1 }
  },
  "output": { "name": "<output-name>" }
}

4. Submit and poll — rl submit has no --name flag (the platform auto-generates the rl-<hex> job id), so derive it after submit:

nemo customization rl submit /tmp/job.json --workspace default   # add --profile <name> if the default gpu profile is wrong
JOB=$(nemo jobs list -f json | python3 -c "import sys,json;d=json.load(sys.stdin);items=d.get('data',d) if isinstance(d,dict) else d;rl=[j for j in items if str(j.get('name','')).startswith('rl-')];rl.sort(key=lambda j:j.get('created_at',''),reverse=True);print(rl[0]['name'] if rl else '')")
bash plugins/nemo-customizer/src/nemo_customizer/skills/nemo-customizer/scripts/poll_customization_job.sh "$JOB"

Do not use 2>&1 before json.load — warnings on stderr break parsing; see Gotchas. Or poll manually: nemo jobs get-status rl-<job-id> every 30–60s. If submit fails on an unknown profile, re-list execution profiles and pass --profile <name>. nemo customization rl run … is disabled (no local execution — it provisions a Ray cluster); nemo customization rl explain prints the live schema.

Defaults

Shared:

Field	Value
Workspace	default
Plugin	automodel (override per Plugin pick)
Training	SFT + LoRA, max_seq_length 2048
Schedule	epochs ≥ 1; omit max_steps
Auth email (when login required)	[e-mail] unless user specifies

Automodel-specific:

Field	Value
Parallelism	1 node, 1 GPU, TP=1
Batch	global_batch_size 4, micro_batch_size 1 (unknown VRAM; see references/batch-sizing.md for ≥48 GB)
Optimizer	learning_rate 5e-5

Unsloth-specific:

Field	Value
Hardware	hardware.gpus "0", hardware.precision bf16 (selection only, single GPU)
Model load	load_in_4bit: true, dtype: "auto"
Batch	batch.per_device_train_batch_size 2, batch.gradient_accumulation_steps 4 (effective batch 8; see references/batch-sizing.md for ≥48 GB ramp)
Optimizer	learning_rate 5e-5, optim adamw_8bit
Output	save_method: "lora" (adapter-only) unless user asks for merged checkpoint
Gradient checkpointing	training.use_gradient_checkpointing: "unsloth"

rl-specific (DPO):

Field	Value
Training	DPO, full-weight (type: "dpo"; no LoRA)
Model (if user gives none)	Qwen/Qwen3-0.6B
Dataset (if user gives none)	nvidia/HelpSteer3 (preference subset; uploaded raw — see references/dataset-formats.md § NeMo-RL)
Schedule (if user gives none)	small demo run: max_steps 20 (completes fast, proves the pipeline). For a real run, set epochs and omit max_steps.
Parallelism	1 node, 1 GPU (parallelism.num_nodes/num_gpus_per_node)
Batch	batch_size 32, micro_batch_size 1
Optimizer	learning_rate 5e-6 (DPO uses a low LR), AdamW + cosine
DPO	ref_policy_kl_penalty (β) 0.05, sft_loss_weight 0.0
Max sequence length	1024
Output	full-weight model entity (output.name); no adapter

When the user asks for a DPO job without specifics, default to the above: a 20-step run of Qwen/Qwen3-0.6B on nvidia/HelpSteer3 — small enough to finish quickly and confirm the pipeline end-to-end.

Batch sizing

micro_batch_size / global_batch_size (automodel) and per_device_train_batch_size × gradient_accumulation_steps (unsloth) on ≥48 GB GPUs, multi-GPU (data vs tensor parallel), and OOM / throughput tuning live in references/batch-sizing.md. On unknown VRAM the Defaults above are safe — read batch-sizing before raising batch on a known ≥48 GB card. rl (DPO) batch knobs (batch_size / micro_batch_size) are in references/hyperparameters-rl.md.

Worked example

Automodel: Qwen/Qwen3-1.7B + tau/commonsense_qa → CHAT JSONL, fileset commonsense_qa, entity qwen3-1.7b, output qwen3-1.7b-commonsense-qa-lora, epochs: 1 (no max_steps). On ≥48 GB GPU use LoRA ≤4B default: micro 32, GBS 128, learning_rate 1e-4 (high-util: 64 / 256).

Unsloth: same model + dataset + entity + fileset, but nemo customization unsloth submit /tmp/job.json -w default. Job JSON ≤4B row: batch.per_device_train_batch_size 8, batch.gradient_accumulation_steps 16 (effective 128), learning_rate 1e-4, hardware.gpus "0", output.save_method "lora". Poll unsloth-<job-id> to completion. Reference fixture: plugins/nemo-unsloth/tests/fixtures/minimal_unsloth_sft.json (ignore max_steps for real runs).

rl (DPO): the no-details default — Qwen/Qwen3-0.6B + nvidia/HelpSteer3 (preference subset, uploaded raw), output qwen3-0.6b-dpo. First confirm kubernetes_job backend (see Plugin pick → rl runtime gate). Upload training.jsonl + validation.jsonl to one fileset, register the model entity, then submit a small 20-step demo job:

{
  "model": "default/qwen3-0.6b",
  "dataset": "default/helpsteer3-dpo",
  "training": { "type": "dpo", "max_steps": 20, "batch_size": 32, "micro_batch_size": 1,
                "learning_rate": 5e-6, "max_seq_length": 1024, "ref_policy_kl_penalty": 0.05,
                "parallelism": { "num_nodes": 1, "num_gpus_per_node": 1 } },
  "output": { "name": "qwen3-0.6b-dpo" }
}

nemo customization rl submit /tmp/job.json -w default, derive the rl-<hex> id (submit has no --name), poll to completion. Reference fixture: plugins/nemo-rl/tests/fixtures/minimal_dpo.json. For a real run, replace max_steps: 20 with epochs.

Report to user

After polling reaches a terminal status (completed, error, or cancelled), report using the template in references/reporting.md — one format for all backends. It covers the Fine-tune result header, the Training configuration table (with per-backend examples: automodel, unsloth, rl/DPO), and Using the adapter (automodel/unsloth LoRA) vs Using the fine-tuned model (full SFT / merged / rl DPO), plus metrics extraction, notes by status, /tmp report saving, and error follow-ups.

Reference files

When	Read
HF conversion or MCQA shaping	references/hf-conversion.md
CHAT vs SFT vs CUSTOM (automodel); text vs messages (unsloth); preference triples (rl/DPO)	references/dataset-formats.md
Field glossary, full JSON template, distillation/KD, live-schema pointers (index routes per backend)	references/hyperparameters.md → hyperparameters-automodel.md / hyperparameters-unsloth.md / hyperparameters-rl.md
Batch sizing (≥48 GB), OOM / throughput (automodel + unsloth)	references/batch-sizing.md
Multi-GPU same node	references/batch-sizing.md § Multi-GPU (same node) (unsloth is single-GPU)
Reporting: result template, Training configuration, Using the adapter / fine-tuned model	references/reporting.md
Backend choice, execution profiles, submit failure, container images, missing image on remote platform, gated HF auth / download 502, CLI, connection errors	references/troubleshooting.md (§ Parsing CLI JSON for 2>&1 / json.load; § Gated HuggingFace models for hf-token)
rl (DPO) needs Kubernetes job execution — verifying / configuring runtime: kubernetes + kubernetes_job executors (local platform → remote cluster, launcher image, PVC, loopback)	references/rl-kubernetes-runtime.md
Live JSON schema	uv run nemo customization automodel explain / uv run nemo customization unsloth explain / uv run nemo customization rl explain
Job JSON fixture (automodel, minimal)	plugins/nemo-automodel/tests/fixtures/qwen3_0.6b_sft_lora.json (ignore max_steps for real runs)
Job JSON fixture (unsloth, minimal)	plugins/nemo-unsloth/tests/fixtures/minimal_unsloth_sft.json (ignore max_steps for real runs)
Job JSON fixture (rl / DPO, minimal)	plugins/nemo-rl/tests/fixtures/minimal_dpo.json (ignore max_steps for real runs)
Job JSON fixture — integrations (W&B / MLflow)	automodel: plugins/nemo-automodel/tests/fixtures/integrations_wandb_mlflow.json · unsloth: plugins/nemo-unsloth/tests/fixtures/integrations_wandb_mlflow.json · rl: plugins/nemo-rl/tests/fixtures/integrations_wandb_mlflow.json
Automodel compile-path contract configs	services/automodel/tests/contract/input_configs/ → YAML in output_configs/ (legacy TrainingStepConfig shape, not submit JSON)
W&B / MLflow field reference (all backends)	references/hyperparameters.md § Integrations (all backends)
W&B secret + MLflow local server + jobs-launcher	references/integrations-setup.md
Gated HF model auth (hf-token, fileset token_secret)	references/troubleshooting.md § Gated HuggingFace models
Post-training eval (base vs LoRA, CHAT format parity)	references/post-training-eval.md, references/eval_helpers.py

Related: plugins/nemo-automodel/README.md, plugins/nemo-unsloth/README.md, plugins/nemo-rl/README.md, docs/customizer/nemo-rl-dpo-plugin-design.md, plugins/nemo-customizer/docs/CUSTOMIZATION.md, skills nemo-files, nemo-status, nemo-secrets.