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.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
A direct command skips the review prompt. Inspect the source before running it.
["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), DPO, and GRPO (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) or GRPO (Gym env)
Platform Kubernetes executor — provisions a Ray cluster; 4 steps (download → train → upload → model-entity)
Preference DPO (full-weight), or GRPO with an environment FileSet (native-v1 / wheels-v1 / adapter-wheels-v1) + Gym rollout rows; GRPO also supports LoRA
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
ifcommand -v nemo >/dev/null 2>&1; thenecho"nemo"elifcommand -v uv >/dev/null 2>&1 && uv run nemo --help >/dev/null 2>&1; thenecho"uv run nemo"elseecho"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 admin@example.com>
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 admin@example.com 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
Run nemo jobs list-execution-profiles -f json (login first only if auth is enabled — see Authentication; see references/troubleshooting.md for parsing).
If the task is DPO / preference optimization (a {prompt, chosen, rejected} dataset, "align", "preference", "RLHF-style"), GRPO / Gym / Prime Intellect env, or the user explicitly asked for NeMo-RL → rl (requires a GPU profile andplatform.runtime: kubernetes).
Else if the user explicitly asked for Unsloth → unsloth.
Else if the user explicitly asked for Automodel → automodel.
Else if any profile has provider: gpu or gpu_distributed → automodel (default, SFT/LoRA).
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 and GRPO need a Kubernetes-runtime platform — do not start/reuse a docker-runtime platform, and do not fall back to automodel/unsloth (those are SFT/LoRA, neither DPO nor GRPO). 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.
Test fixtures are not the schema.tests/fixtures/*.json are smoke-test inputs: they carry whatever made a test cheap, exercise one path rather than the field set, and nothing fails when the schema gains a field they never set. Read one for where a block sits in the payload — never for which fields exist, what a default is, or what a sensible value looks like, and never conclude a field is unsupported because a fixture omits it. Authoritative, in order: nemo customization <plugin> explain (the installed build's live schema), then the schema source files in references/hyperparameters.md § Source of truth, then this skill. When a fixture and explain disagree, the fixture is stale — say so rather than following it.
Never set max_steps together with epochs (automodel + unsloth; rl has the same caveat — see rl (DPO / GRPO) gotchas). max_steps is a global cap and stops mid-epoch. Every fixture in this repo sets it so a smoke test finishes in a minute — the most-copied wrong value here. 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 pollingnemo 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-customizer-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 (automodel/unsloth save_method: lora, or rl GRPO with finetuning_type: "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 / GRPO) gotchas
rl is DPO or GRPO, not SFT — DPO trains on preference pairs{prompt, chosen, rejected}; GRPO needs an environment FileSet + a Gym rollout-row dataset. Don't route SFT/LoRA work here, and don't route DPO/GRPO to automodel/unsloth.
DPO is full-weight only; GRPO does LoRA too — set finetuning_type: "lora" on the GRPO training block (plus an optional lora block). The output type is inferred, so output still carries only name. Three things the schema enforces: lora must be omitted when finetuning_type is all_weights; lora_merged is rejected outright (no merge at export — train full-weight if merged weights are the goal); and use_triton is forced off when tensor_parallel_size > 1, so leave it alone. Fields and module-selection rules: references/hyperparameters-rl.md § LoRA (GRPO only).
There is no grpo subcommand — GRPO submits through nemo customization rl submit like DPO, selected by training.type: "grpo" in the job JSON. training.type is the union discriminator and is required: omitting it fails with union_tag_not_found rather than defaulting.
GRPO needs TWO FileSets — an environment (code + config, purpose=environment) and a dataset (prompt rows, purpose=dataset). Both are plain string refs in the job JSON. Three environment formats are supported — native-v1, wheels-v1, adapter-wheels-v1 — and picking one is the first question to settle. Full guide: references/gym-environments.md.
Never put .jsonl in the environment package — validation rejects it outright. This bites the native-v1 path especially: Gym's own configs point datasets[].jsonl_fpath at an in-tree file, so an environment copied straight from the Gym source tree fails until the data dir is stripped and the prompts move to the dataset FileSet.
Validate the environment package before uploading — uv run --package nmp-rl pi-to-gym-conversion --validate-only <dir> prints {"valid": true, ...} or exits 1 with the exact violation. The same checks run at submit against the FileSet listing, so this catches the failure minutes earlier and for free.
GRPO convert is CLI-first — run pi-to-gym-conversion on a host with internet; training clusters consume uploaded FileSets only (no hub egress). Pin --hub-version: unset takes whatever the index offers now, and a later release can narrow Requires-Python or ship code the training image's Python cannot run. sandboxed is platform config (NMP_RL_SANDBOXED_GYM_DEFAULT, default true), not a job JSON field.
GRPO progress is read on reward, not loss — the GRPO surrogate loss oscillates around zero and carries no signal about run quality. Report train_reward and val_accuracy (NeMo-RL's name for the validation pass's mean reward, not an accuracy in the classifier sense — there is no val_reward). When reward stalls, look at train_truncation_rate (rising) and train_baseline_reward/pct_mixed (falling toward zero means every prompt group agrees with itself, so there is no gradient left). See references/reporting.md.
One preference fileset, two files (DPO) — dataset is a single string ref to a fileset that holds bothtraining.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, dataset, and (for GRPO) environment are plain strings ("workspace/name"), not objects. The training method goes under training with type: "dpo" or "grpo".
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-customizer-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). Read the job id from the "name" field in submit stdout (JSON), same as automodel/unsloth; poll_customization_job.sh rl-<id> works. Do not pick the newest rl-* from nemo jobs list — a concurrent job or an earlier failed submit selects the wrong one. If submit stdout could not be parsed, stop and re-check rather than guessing a job id.
DPO main knob is ref_policy_kl_penalty (β). For OOM, enable activation_checkpointing: true first. Full field reference: references/hyperparameters-rl.md.
GRPO main knobs are num_generations_per_prompt (group size — the spread of rewards inside a group is the whole learning signal) and temperature (must stay > 0; greedy sampling makes every rollout in a group identical and the run a no-op). For OOM, enable activation_checkpointing: true, then lower num_generations_per_prompt keeping batch_size divisible.
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 the JSONL files, nemo files list to verify — automodel/unsloth: train.jsonl (+ validation.jsonl); rl: training.jsonl + validation.jsonl (see rl branch)
- [ ] 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>]
- [ ] Read job id from the "name" field in submit stdout (JSON) — submit has no --name flag; do NOT pick the newest rl-* from `nemo jobs list`
- [ ] Poll until top-level terminal (`poll_customization_job.sh rl-<job-id>`; default 15s interval)
- [ ] Report using the template in `references/reporting.md`
# rl branch (GRPO; submit → Kubernetes/Ray job) — requires platform.runtime: kubernetes
- [ ] Verify execution backend (same gate as DPO above)
- [ ] Pick the ENVIRONMENT format — verifiers/hub env → adapter-wheels-v1; Gym source tree → native-v1; own code → wheels-v1 (see references/gym-environments.md)
- [ ] Build the environment package: hub env → `pi-to-gym-conversion --hub-id … --hub-version … --out-dir …` on an internet-capable host; otherwise hand-build the manifest + configs (+ wheels for the two wheels formats)
- [ ] Validate BEFORE upload: `pi-to-gym-conversion --validate-only <pkg-dir>` — cheapest possible failure
- [ ] Upload environment (--purpose environment) and dataset (--purpose dataset) as TWO filesets; preserve relative paths; no .jsonl inside the env package
- [ ] Dataset rows are GYM ROLLOUT ROWS (prompt under responses_create_params.input + agent_ref object) — see references/dataset-formats.md § NeMo-RL (GRPO)
- [ ] Write /tmp/job.json with training.type "grpo" + the `environment` string ref (see Fast path — rl (GRPO))
- [ ] nemo customization rl submit /tmp/job.json --workspace default [--profile <gpu-profile>]
- [ ] Read job id from the "name" field in submit stdout (JSON) — submit has no --name flag
- [ ] Poll until top-level terminal (`poll_customization_job.sh rl-<job-id>`)
- [ ] Report on REWARD, not loss (references/reporting.md)
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.
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.
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.
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 (GRPO)
GRPO on a Ray cluster — Kubernetes runtime only, full-weight. Same runtime gate as DPO: confirm nemo jobs list-execution-profiles -f json shows cpu/gpu at backend: kubernetes_job before anything else.
GRPO differs from every other backend in one structural way: it needs two FileSets, an environment (code that runs a rollout and returns a reward) and a dataset of prompt rows. There are no labelled completions — the reward comes from the environment.
1. Environment — the bulk of the work, and it has its own reference: references/gym-environments.md. Pick the format first:
The user has…
Format
How
A Prime Intellect hub env, or any verifiers env
adapter-wheels-v1
pi-to-gym-conversion (below) — the supported path
An env already in the NeMo Gym source tree
native-v1
Package the server dir + add a manifest; strip any .jsonl
--upload creates both FileSets and uploads them. Without it, upload by hand (--purpose environment for the package, --purpose dataset for the JSONL) — see references/gym-environments.md § Upload. Always validate first: pi-to-gym-conversion --validate-only ./ascii-tree-pkg.
2. Dataset — the converter writes it for a hub env. For the user's own prompts, rows are Gym rollout rows with the prompt under responses_create_params.input and an agent_ref object — notmessages[], not prompt/completion, not preference triples. Schema and a conversion snippet: references/dataset-formats.md § NeMo-RL (GRPO).
3. Model — same as automodel Fast path step 2 (HF weights fileset + model entity; gated repos need token_secret).
4. Job JSON — model, dataset, and environment are all strings; the method is training.type: "grpo". Full field reference: references/hyperparameters-rl.md.
LoRA variant. GRPO trains full-weight by default. To train an adapter instead, add finetuning_type and an optional lora block — nothing else changes:
"training":{"type":"grpo","finetuning_type":"lora","lora":{"rank":16,"alpha":32},"...":"rest as above"}
The output type is inferred — lora registers an HF PEFT adapter entity against the base model, and output still carries only name. Do not set lora alongside all_weights (hard error), and do not ask for lora_merged (rejected; GRPO does not merge at export). Field table and module-selection rules: references/hyperparameters-rl.md § LoRA (GRPO only).
5. Submit and poll — identical to DPO (no --name; read the rl-<hex> id from submit stdout):
Read GRPO progress on reward, not loss. The GRPO surrogate loss oscillates near zero and says nothing about run quality. train_reward and val_accuracy are the curves that matter — val_accuracy is the validation pass's mean reward under NeMo-RL's name for it. train_truncation_rate rising or train_baseline_reward/pct_mixed falling toward zero explain a reward curve that stopped moving. See references/reporting.md § GRPO metrics.
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:
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.
4. Submit and poll — rl submit has no --name flag (the platform auto-generates the rl-<hex> job id), so read it from submit stdout:
nemo customization rl submit /tmp/job.json --workspace default > /tmp/rl-submit.json # add --profile <name> if the default gpu profile is wrong
JOB=$(python3 -c "import json;print(json.load(open('/tmp/rl-submit.json'))['name'])")
bash plugins/nemo-customizer/src/nemo_customizer/skills/nemo-customizer/scripts/poll_customization_job.sh "$JOB"
Do not derive the job id by picking the newest rl-* from nemo jobs list — a concurrent job or an earlier failed submit selects the wrong one. If /tmp/rl-submit.json does not parse, the submit result is unknown: stop and inspect it (re-submitting risks a duplicate 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)
admin@example.com 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.precisionbf16 (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, optimadamw_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 omitmax_steps.
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.
rl-specific (GRPO):
Field
Value
Training
GRPO (type: "grpo"), full-weight by default (finetuning_type: "all_weights"). Use finetuning_type: "lora" when the user asks for an adapter or the full-weight run will not fit; lora_merged is rejected.
Model (if user gives none)
Qwen/Qwen3-0.6B
Environment (if user gives none)
primeintellect/ascii-tree converted with pi-to-gym-conversion — small, self-contained, no judge model needed
Environment format
adapter-wheels-v1 (converter output) unless the user's env dictates otherwise
Schedule (if user gives none)
small demo run: max_steps 20. For a real run, set epochs and omitmax_steps.
Parallelism
1 node, 1 GPU
Batch
batch_size 32, micro_batch_size 1
Rollout
num_generations_per_prompt 8 (must divide batch_size), temperature 1.0
Optimizer
learning_rate 1e-6 (GRPO uses a very low LR — lower than DPO)
Max sequence length
2048
Validation
val_at_start: true when the user wants to see uplift; costs one extra rollout pass
Output
full-weight model entity (output.name); with finetuning_type: "lora", an HF PEFT adapter entity instead — the type is inferred, never set
LoRA (when used)
rank 16, alpha 32, dropout 0.0, all linear layers (omit target_modules/exclude_modules)
When the user asks for a GRPO job without specifics, offer the ascii-tree demo
above and say plainly that GRPO needs an environment — it cannot be run from a
dataset alone, unlike every other backend here.
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.
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_rate1e-4, hardware.gpus"0", output.save_method"lora". Poll unsloth-<job-id> to completion. For payload shape only (not the field set or values): plugins/nemo-unsloth/tests/fixtures/minimal_unsloth_sft.json — a smoke-test input, so confirm fields against unsloth explain.
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:
nemo customization rl submit /tmp/job.json -w default, derive the rl-<hex> id (submit has no --name), poll to completion. For payload shape only: plugins/nemo-rl/tests/fixtures/minimal_dpo.json — a smoke-test input, so confirm fields against rl explain. 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); Gym rollout rows (rl/GRPO)
references/dataset-formats.md
GRPO environments — the three formats (native-v1, wheels-v1, adapter-wheels-v1), converting a verifiers / Prime Intellect env, packaging a Gym source-tree env, bringing your own code, validation, upload, packaging errors
references/gym-environments.md
Field glossary, full JSON template, distillation/KD, live-schema pointers (index routes per backend)
