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ShadowLM examples

Runnable, one-file examples — one per (backend × method). Each script is the same shape: load a dataset, slm.load(...) a model on that backend, finetune with that method, print the loss, and save. Switching method or backend is the one-word change ShadowLM is built around.

Run any of them from the repo root:

python examples/mlx/lora.py
python examples/torch/qlora.py
python examples/remote/grpo.py

Models

  • mlx/ uses Qwen2.5-0.5B (the Apple-Silicon dev loop — small and fast).
  • torch/, remote/ use Qwen3-8B (the CUDA / GPU paths).

Backend × method coverage

method mlx torch remote dataset base requirement
lora ✅ ✅ ✅ chat —
qlora ✅ ✅ ✅ chat 4-bit base
dora ✅ ✅ ✅ chat —
full ✅ ✅ ✅ chat unquantized
cpt ✅ ✅ ✅ raw text —
dpo ✅ ✅ ✅ preference pairs —
grpo ✅ ✅ ✅ prompts + reward fn —
more ✅ ✅ ✅ facts —
more_plus ✅ ✅ ✅ facts unquantized
bitfit ✅ ✅ ✅ chat unquantized + bias params
prompt — ✅ ✅ chat torch only
ptuning — ✅ ✅ chat torch only
adapter ✅ ✅ ✅ chat —

Notes:

  • mlx runs every method except the soft-prompt family (prompt, ptuning), which it routes to torch.
  • remote forwards each method to a ShadowLM server over the JSON protocol; the method support is whatever the server's backend provides. Point SHADOWLM_API_URL at your server (or run one locally with shadowlm serve).
  • bitfit on the 8B examples: Qwen3 dropped QKV biases (it uses QK-norm), so bitfit has nothing to train there — the examples note this and point you to a base that has biases (e.g. Qwen/Qwen2.5-7B-Instruct).

Shared data

The data/ folder holds tiny sample datasets so the examples are self-contained:

file format used by
data/chat.jsonl chat (messages) lora, qlora, dora, full, bitfit, prompt, ptuning, adapter
data/preference.jsonl preference (prompt/chosen/rejected) dpo
data/domain.jsonl raw text (text) cpt
data/facts.jsonl instruction (instruction/output) more, more_plus

grpo defines its prompts and reward function inline in each script.

There's also shadowlm_qa.jsonl — a chat dataset about ShadowLM itself, handy for a quick end-to-end finetune that teaches a small model to answer questions about the SDK.