GitHub - stepfun-ai/SteptronOss: A lightweight, AI-native training framework for large language models. Designed for fast iteration, reproducible experiments, and modular configuration across SFT, RLVR, and evaluation workflows.

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StepTronOSS is a lightweight training framework for large-scale language models with a focus on modular configs, reproducible experiments, and fast iteration across SFT, RLVR, and evaluation workflows. It can run with only PyTorch as a dependency, while also supporting operator-level replacements for acceleration.

Key capabilities:

• Config-driven experiments with dynamic validation (cfshow, sanity_check)
• Multi-task orchestration with flexible launch tooling
• Extensible data/optimizer/model stacks for rapid research iteration

Docs

• Launch guide (EN): docs/LAUNCH_EXPERIMENTS.md
• Launch guide (ZH): docs/LAUNCH_EXPERIMENTS_ZH.md
• SFT data prep (ZH): docs/SFT_DATA_PREPARATION.md
• SFT data prep (EN): docs/SFT_DATA_PREPARATION_EN.md
• API modules: docs/MODULES.md

Installation

# from repo root
uv sync
# install redis-server
apt install -y redis-server

Getting Started

uv virtual environment is recommanded. If not, prefix with uv run.

Experiment Overview & Sanity Check

# Overview the experiment config and run sanity_check
uv run cfshow playground/rlvr/qwen3_1p5b_rlvr_math.py
# Inspect a specific subtree (e.g., actor config)
uv run cfshow playground/rlvr/qwen3_1p5b_rlvr_math.py -k actor_model_cfg

Run Experiments

# Single-task experiments (e.g., SFT)
uv run torchrun playground/sft/your_exp.py

# Multi-task experiments (e.g., RL)
export STEPTRON_MEET_DIR=/path/to/shared
uv run tools/mp_run.py playground/rlvr/qwen3_1p5b_rlvr_math.py

# mp_run is also compatible with single-task experiments
uv run tools/mp_run.py playground/sft/your_exp.py

# Override experiment params (example: enable timer logging)
uv run tools/mp_run.py playground/rlvr/qwen3_1p5b_rlvr_math.py profiler_cfg.timing_log_level=1

See the docs section above for detailed launch guides.

Generate Multi-Node Launch Scripts

Use tools/build_scripts.py to generate per-replica shell scripts based on resource_cfg.task_specs.

# Example: generate scripts under /mnt/entrypoints/<exp_name>/<exp_id>/
uv run tools/build_scripts.py playground/rlvr/qwen3_1p5b_rlvr_math.py /mnt/entrypoints/

Output layout:

/mnt/entrypoints/<exp_name>/<exp_id>/
  cpu/
    0.sh
  gpu/
    0.sh
    1.sh
    ...

Notes:

• Script indices are cumulative within each node type (e.g., all GPU tasks share a single 0..N index range).
• Each script exports task envs (including task-scoped EXP_ID and NNODES) and invokes the command assembled by mp_run.
• Extra CLI args are passed through to the experiment, just like mp_run.py.

Runtime Environment

Distributed rendezvous spins up a per-experiment Redis server using a shared filesystem directory.

• STEPTRON_MEET_DIR: shared directory visible and writable by all nodes. It stores the rendezvous file that publishes the Redis server port.
• CANNOT_BE_REDIS_SERVER=1: set on ranks that must not start Redis (they will wait for another rank to start it). If every rank sets this, rendezvous will eventually time out.

Zen

Core principles:

• Keep configs stateless; runtime objects carry state.
• Declare config structure at class level, fill values in __init__.
• Use Ref("..path") for cross-node linkage.
• Call sanity_check() and to_dict() for validation/serialization.

Configurize example:

# Before
class A:
    def __init__(self, param_a: int, param_b: float = 1.0):
        pass

# After
from configurize import Config

class AConfig(Config):
    param_a: int
    param_b: float = 1.0

    def build(self):
        return A(cfg=self)

    def sanity_check(self):
        super().sanity_check()
        assert self.param_b > 0

class A:
    def __init__(self, cfg: AConfig):
        pass

AI Native

StepTronOSS enables an AI-native workflow: its modular architecture supports easy verification and iterative development, while tools like cfshow provide dynamic config inspection and validation. The repo also ships with AGENTS.md to guide agents in contributing to code changes.

Try asking your agent:

write an exp of qwen3 8B sft, optimizer use muon

Optimization Kernels

Only needed when you want maximum GPU performance and kernel-level speedups.

flash-attn

Install manually:

uv pip install flash-attn --no-build-isolation

grouped_gemm

Install manually:

uv pip install --verbose git+https://github.com/fanshiqing/grouped_gemm@main

Enable in code (set all optimizations at once)

from steptronoss.utils.optimizable import set_optimization

set_optimization(
    default="torch_compile",
    AttentionCore="flash-attn",
    grouped_gemm="nv_grouped_gemm",
)

Project Status

• SFT exps
• Reference configs: Qwen3 8B playground/pretrain/qwen3/qwen3_8.py, Step3.5 Flash playground/pretrain/step3p5/step3p5_flash.py
• Eval
• RLVR implementation
• Triton kernel implementation