> ## Documentation Index
> Fetch the complete documentation index at: https://starforge.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Chain runs into a pipeline

> init-from relay training, --then automatic post-processing, export and eval

```bash theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}}
sf submit my-sft --profile h200:8
sf submit my-dpo --profile h200:8 --init-from run/<SFT_RUN_ID>/checkpoint
```

Post-training is rarely one step: SFT for the base, then DPO or GRPO alignment, then export and
evaluation. `--init-from` is how a stage starts from the previous one's artifact.

Every run's checkpoint is registered automatically, the next stage references it by run id, and the
lineage stays queryable end to end — so six months later "what was this trained from" has an answer
that does not depend on anyone's memory.

## Start from the previous stage: --init-from

```bash theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}}
# Stage 1: SFT
sf submit my-sft --profile h200:8
# → on success the artifact is registered as run/<SFT_RUN_ID>/checkpoint

# Stage 2: DPO starting from the SFT artifact
sf submit my-dpo --profile h200:8 \
  --init-from run/<SFT_RUN_ID>/checkpoint

# Pin a specific step's checkpoint
sf submit my-grpo --profile h200:8 \
  --init-from "run/<DPO_RUN_ID>/checkpoint@step=120"
```

The server resolves the reference, verifies the artifact exists and its ownership, and injects the actual path into the job. The **Lineage** view in the console job details shows the entire chain.

## Run automatically after training succeeds: --then

```bash theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}}
sf submit my-grpo --profile h200:8 --then export --then eval
```

After training succeeds, the platform automatically submits the post-processing jobs (seconds to minutes — no extra RayCluster cold start to pay for).

## Export to HuggingFace format: sf export

```bash theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}}
sf export my-grpo \
  --checkpoint <checkpoint path from the artifact registry> \
  --checkpoint-format nemo-dcp \
  --push-repo myorg/my-model        # optional: push to HF Hub (requires a linked HF account)
```

`--checkpoint-format` supports `nemo-dcp` / `nemo-megatron` / `verl-fsdp` / `verl-megatron` / `huggingface`. When pushing to the Hub, the server injects the **current user's** HF token (see [HuggingFace Integration](/en/integrations/huggingface)); the repo name is validated against injection.

## Framework-native evaluation: sf eval

Runs the framework-native evaluation entry point declared by the recipe:

```bash theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}}
# NeMo-RL: evaluate an HF model
sf eval my-grpo --model /shared/hf_export/my-model
# NeMo-RL: explicit evaluation config
sf eval my-grpo --model Qwen/Qwen3.5-9B --eval-config configs/eval/gsm8k.yaml

# verl SFT: report validation samples back to the training run
sf eval my-verl-sft --run-id <TRAIN_RUN_ID> --step 200 --data data/val.parquet
```

<Note>
  `sf eval` is **framework-native** evaluation (reusing the training framework's inference path); for **standard benchmarks** comparable across models (GSM8K / MMLU / C-Eval…) use `sf bench`, whose scores are ingested into the unified dashboard. See [Benchmarks](/en/guides/benchmarks).
</Note>

## Cleanup: sf clean

```bash theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}}
sf clean my-old-exp        # interactive confirmation
sf clean my-old-exp -y     # skip confirmation
```

Deletes the experiment's checkpoints and logs on the cluster — **irrecoverable**. Job records and metrics are retained on the platform.

## A complete pipeline example

```bash theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}}
# 1. SFT as the base
sf new sft-qwen95 --method nemo-rl/sft
sf submit sft-qwen95 --profile h200:8
# 2. GRPO alignment (chained to the SFT artifact, auto-export after training)
sf new grpo-qwen95 --method nemo-rl/grpo
sf submit grpo-qwen95 --profile h200:8 \
  --init-from run/<SFT_RUN>/checkpoint --then export
# 3. Standard benchmark verification
sf bench run bench-qwen95 -m run:<GRPO_RUN> --suites gsm8k,mmlu
# 4. Chat with it in the Playground
sf serve start run:<GRPO_RUN> --gpus 1
```