toml-config

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• SKILL.md

SKILL.md (first lines)

# TOML Config

All prime-rl commands use `pydantic_config` (tyro-backed) with TOML configs and CLI overrides.

## Running with configs

```bash
# Load a config file with @ syntax
uv run inference @ configs/debug/infer.toml
uv run sft @ configs/debug/sft/train.toml
uv run rl @ configs/debug/rl/train.toml

# CLI overrides (take precedence over TOML)
uv run inference @ config.toml --model.name Qwen/Qwen3-0.6B --server.port 8001

# Boolean flags: no value needed
uv run inference --model.enforce-eager          # sets to true
uv run inference --no-model.enforce-eager       # sets to false

# CLI-only (no TOML file)
uv run inference --model.name Qwen/Qwen3-0.6B --model.max-model-len 2048

# Compose multiple config files (later files override earlier ones)
uv run rl @ examples/reverse_text/rl.toml @ examples/reverse_text/slurm_rl.toml

# Nested config files: load a config for a specific section
uv run rl --model @ model.toml --data @ data.toml
```

## TOML structure

Top-level fields must come before any `[section]` header — this is a TOML rule.

```toml
# Top-level fields first
gpu_memory_utilization = 0.5
seed = 42

# Then sections
[model]
name = "Qwen/Qwen3-0.6B"
max_model_len = 4096

[server]
port = 8000
```

Putting a top-level field after a section header nests it inside that section, which causes validation errors.

## Setting None

Use the string `"None"` in TOML to set a field to None:

```toml
max_model_len = "None"
```

## SLURM mode

Both `rl` and `sft` commands support SLURM execution via an optional `[slurm]` section. When present, the run is submitted as a SLURM job instead of running locally.

SLURM configs are composed with the base config via CLI:
```bash
uv run rl @ examples/reverse_text/rl.toml @ examples/reverse_text/slurm_rl.toml
```

### RL SLURM

```toml
output_dir = "/shared/experiments/my-run"

[deployment]
type = "multi_node"
num_train_nodes = 2
num_infer_nodes = 1
gpus_per_node = 8
# nodes_per_fsdp_group = 1

[slurm]
job_name = "my-rl-job"
# dry_run = true          # generate script without submitting
# template_path = "path/to/custom.sh.j2"
# project_dir = "/path/to/project"
```

When `[slurm]` is set for RL:
- `output_dir` must be explicitly set (the default `outputs` is rejected)
- Teacher inference is not supported in multi-node deployment

### SFT SLURM

```toml
output_dir = "/shared/experiments/my-sft-run"

[deployment]
type = "multi_node"
num_nodes = 2
gpus_per_node = 8
# nodes_per_fsdp_group = 1

[slurm]
job_name = "my-sft-job"
# dry_run = true
# template_path = "path/to/custom.sh.j2"
# project_dir = "/path/to/project"
```

SFT deployment follows the same pattern as RL:
- `[deployment]` configures node/GPU allocation (`single_node` default or `multi_node`)
- `[slurm]` configures SLURM submission (job name, partition, template)
- `output_dir` must be explicitly set when using SLURM
- Multi-node deployment requires `[slurm]` to be set

## Available commands

All accept `@ config.toml` and CLI overrides:

| Command | Config class | Description |
|---------|-------------|-------------|

From leodr/posttraining, Apache-2.0.