autoslm-train 0.1.0

Managed consumer-GPU LoRA post-train package on RunPod Flash

AutoSLM — managed consumer-GPU LoRA post-train

AutoSLM is a managed fine-tuning package for LoRA fine-tuning (SFT + GRPO) of small models. It gives a focused developer experience — discover an environment, point a TOML at it, train, evaluate, deploy — while the infrastructure is fully managed: every run is assigned its own dedicated GPU on RunPod Flash (serverless, no Docker), restricted to RTX 4090 / RTX 5090.

• Managed infra, one GPU per run. No backend to configure, no Docker images, no cluster. Flash provisions the GPU, installs deps, runs your job, and scales to zero.
• Composable, interoperable environments. A simple TOML/config mental model; environments interoperate with the verifiers / Environments Hub ecosystem.
• uv-native. Install and run everything with uv.

Install

AutoSLM is uv-native, but it's a normal Python package (autoslm-train on PyPI, imports as autoslm, CLI entry point slm), so any of these work:

# As a CLI tool (recommended for just using AutoSLM):
uv tool install autoslm-train  # installs the `slm` command
# or: pipx install autoslm-train / pip install autoslm-train

# From source (for development / latest main):
git clone https://github.com/Eric-Mao06/autoslm   # pragma: allowlist secret
cd autoslm   # pragma: allowlist secret
uv sync                            # create the venv from the lockfile
uv run slm --help

The heavy GPU fine-tuning stack (torch/vllm/trl/peft) is worker-side — RunPod Flash installs it on the GPU for each run, so you don't need it locally. If you want to run the worker yourself, install the extra: uv sync --extra gpu (or pip install "autoslm-train[gpu]").

Requires Python 3.11 or 3.12 and a RunPod account.

(Examples below use uv run slm …; if you installed the CLI with uv tool/pipx/pip, just drop the uv run prefix and call slm ….)

Quickstart

uv run slm login --api-key $RUNPOD_API_KEY     # store your RunPod key (~/.autoslm/config.json)
export HF_REPO=your-org/autoslm-runs               # HF dataset repo for adapters/checkpoints
export HUGGINGFACE_TOKEN=...                   # write access to HF_REPO

uv run slm lab setup                            # scaffold configs/ + environments/
uv run slm models                               # list curated consumer-GPU models
uv run slm train configs/examples/gsm8k_grpo.toml --dry-run   # validate without provisioning
uv run slm train configs/examples/gsm8k_grpo.toml --background
uv run slm status <run_id>
uv run slm eval configs/examples/gsm8k_grpo.toml --adapter <run_id>   # eval base + adapter
uv run slm deploy <run_id>                        # warm a managed GPU serving the adapter
uv run slm chat <run_id> -m "What is 17 * 23?"    # OpenAI-shaped chat via the managed GPU

A config is a single TOML:

model = "Qwen/Qwen3-4B-Instruct-2507"
algorithm = "grpo"            # or "sft"

[environment]
id = "gsm8k"                   # built-in: gsm8k | math | tests_pass (or path = "...")

[train]
steps = 150
lora_rank = 32
seeds = [0]

[gpu]
type = "RTX 5090"             # RTX 4090 or RTX 5090 only (defaults from model size)

How it works

slm train cfg.toml
  └─ autoslm.orchestrator           # one dedicated GPU per run/seed
       └─ autoslm.flash.train       # @Endpoint on RunPod Flash (RTX 4090/5090, no Docker)
            └─ autoslm.engine.worker   # SFT/GRPO + eval on the GPU (TRL + colocated vLLM, PEFT LoRA)
HF dataset repo                # adapter + checkpoints (resume) stream here; metrics return inline

The fine-tuning worker runs two fresh processes on the GPU — train, then eval — so the TRL trainer and the vLLM eval engine never share a GPU allocation. Adapters and checkpoints are streamed to a Hugging Face dataset repo (HF_REPO) for serving and preemption-resilient resume; metrics are returned directly from the Flash call.

Environments

Built-ins implement a small task interface (dataset, prompt_messages, sft_target, reward, grade):

• gsm8k — GSM8K verifiable math with a shared boxed/#### grader.
• math — competition math (DeepScaleR train → MATH-500 eval), LaTeX/numeric grading.
• tests_pass — coding tasks: apply a generated diff and reward on a test command.

Scaffold a custom one with uv run slm env init my-env, then point a config at it via [environment] path = "environments/my_env".

Prime Hub / verifiers interop. AutoSLM runs Prime Intellect verifiers environments unchanged (single-turn). Install one and point a config at it:

uv run slm env install owner/my-env       # via the prime CLI or pip; recorded in ~/.autoslm/envs.json
uv run slm env list                        # shows built-in + installed + local envs

[environment]
id = "owner/my-env"                       # loaded through the verifiers adapter

The GPU worker automatically installs verifiers + the env package for that run. Multi-turn/tool environments are a follow-up.

Layout

autoslm/
  cli/             `slm` CLI (login/train/eval/deploy/status/logs/env/models)
  catalog.py       curated consumer-GPU model catalog
  config_schema.py TOML -> JobSpec (validates RTX 4090/5090)
  orchestrator.py  drives RunPod Flash (one dedicated GPU per run)
  flash/           RunPod Flash integration (gpus, train endpoints, serving, auth, preflight)
  engine/          substrate-neutral internals: recipe, data, grading, accounting, worker
  envs/            environment interface + built-ins (gsm8k, math, tests_pass)
  serve/           adapter serving via Flash (deploy + OpenAI-shaped chat)
  server/          optional FastAPI control plane
  mcp/             stdio tool bridge for coding agents
  _logging.py      package logging helpers (quiet by default; `-v` / `AUTOSLM_LOG_LEVEL`)
tests/             CPU tests (no GPU/network required)

Docs

• Config reference — TOML schema, --set/--config, env vars.
• Environments — authoring + Prime Hub / verifiers interop.

Cold starts

The first run on a fresh worker pulls the image + installs deps (a few minutes) + downloads the model (~8 GB). Set AUTOSLM_WORKER_IMAGE to a prebuilt image with the deps and the base model baked in to skip both:

export AUTOSLM_WORKER_IMAGE=your-registry/autoslm-worker:cu128

Build it (on any Docker host — no GPU needed to build; see docker/):

docker/build.sh your-registry/autoslm-worker:cu128 Qwen/Qwen3-4B-Instruct-2507 --push

The worker still scales to zero when idle (workers=(0,1)) — the image only speeds the cold start, it does not keep a GPU online. The baked model lives in the image's HF_HOME cache, so it loads locally instead of downloading.

Troubleshooting

• error: cannot start a managed run — missing required configuration — set your RunPod key (slm login --api-key <key> or RUNPOD_API_KEY) and export HF_REPO + HUGGINGFACE_TOKEN. Add --dry-run to validate a config without provisioning a GPU.
• error: unsupported model … — the model isn't in the catalog. Run slm models (add --experimental to see the bleeding-edge tier). The default, Qwen/Qwen3-4B-Instruct-2507, is a proven dense model that loads on the pinned worker stack.
• Experimental models fail to load on the worker — the Qwen3.5* / Qwen3.6* entries use newer architectures than the pinned vllm/transformers. Bake a newer stack into a AUTOSLM_WORKER_IMAGE to use them.
• vLLM OOM / KV-cache errors during GRPO — see the colocated-GRPO memory recipe in the config reference.
• Cold starts are slow — bake a prebuilt AUTOSLM_WORKER_IMAGE (see Cold starts).
• See more detail — add -v (info) or -vv (debug) to any command, or set AUTOSLM_LOG_LEVEL=DEBUG. Use --debug (or AUTOSLM_DEBUG=1) to get a full traceback on error.
• Check the version — slm version (or slm --version).

Development

uv sync                       # install dev deps from the lockfile
AUTOSLM_SKIP_NET=1 uv run pytest tests -q   # CPU unit tests (AUTOSLM_SKIP_NET=1 skips live HF)
uv run ruff check .           # lint
uv run ruff format .          # format

See CONTRIBUTING.md for the full guide. CI runs the tests (Python 3.11 + 3.12) and the ruff checks on every push/PR.

License

Apache-2.0.