cwm-extended-adapters

nmk-kun/cwm-extended-adapters

cwm-extended-adapters is a machine learning model from nmk-kun. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as other.

Trained LoRA adapters (r=16, α=32, on attention + MLP projections) for the CWM interactive / visual world-model project. Base model: facebook/cwm (32B Code World Model).

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Updated Jun 30, 2026

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At a glance

Library: peft
License: other
Access: Public
Created: Jun 30, 2026
Updated: Jun 30, 2026
SHA: ea202f04

Base models

facebook/cwm

Model card

From the Hugging Face model README

CWM-Extended — LoRA adapters

Trained LoRA adapters (r=16, α=32, on attention + MLP projections) for the CWM interactive / visual world-model project. Base model: facebook/cwm (32B Code World Model).

📄 Code + full empirical record (results/REPORT.md, §0–§39): https://github.com/namak-kun/cwm-extended — read the REPORT section noted in each row below for the experiment, controls, metrics, and caveats behind every number.

Each subfolder is a standalone, loadable PEFT adapter (adapter_model.safetensors + adapter_config.json).

What each adapter is

🎮 Game world-model — the headline axis (§30, §32)

The "world model" thesis: predict a game's tick-by-tick state evolution from code, execution-free; then bootstrap action-conditioned dynamics from unlabeled state sequences via a forward↔inverse flywheel.

adapter	what it teaches	result	REPORT
`cwm_gametick_stepover`	One-shot game-tick transition `s_i → s_{i+1}` (player + K enemies + within-tick stomp/contact side-effects), via step-over SFT. This is FDM₀, the base the flywheel arms continue-train from.	per-tick state 0.017 → 0.692	§30
`cwm_fdm_idm_r1`	Flywheel round 1: continue-trained from `cwm_gametick_stepover` on self-labeled trajectories (FDM-as-IDM forward-search inverse dynamics — no action labels).	per-tick 0.525 → 0.683 (≈ true-action oracle; CI excludes 0)	§32
`cwm_fdm_idm_r2`	Flywheel round 2: stacks a 2nd self-labeled round (margin-filtered → 99% label recovery). Stable plateau, no collapse.	per-tick 0.683 → 0.696	§32.7
`cwm_fdm_oracle_r1`	Control for `idm_r1`: identical recipe but true-action oracle labels. Confirms self-labeling ≈ oracle.	per-tick ≈ 0.679	§32
`cwm_fdm_oracle_r2`	Control for `idm_r2` (oracle round 2).	per-tick ≈ 0.692	§32.7
`cwm_fdm_hardoracle`	Hard arena (K6–8, where self-labeling collapses to chance): oracle SFT shows the hard ceiling is breakable with oracle/engine labels + a K-curriculum.	per-tick 0.284 → 0.369	§32.10

🖼️ UI / DOM render world-model — the pixel axis (§35, §36)

State = canonical DOM tree (a sufficient statistic for the rendered pixels). These probe cascade/validation logic and abstraction transfer.

adapter	what it teaches	result	REPORT
`cwm_cascade`	Step-over SFT on UI DOM-cascade apps (`ui_dom` + `ui_tick`). In-distribution win, but a cautionary negative transfer to real JS — the main open SFT problem.	uidom exact 0.80 → 1.0; real-JS vanilla 0.75 → 0.35 ⚠️	§36
`cwm_heldapp`	Same UI-cascade SFT but trained without the `togglelist` app, then evaluated on it (different schema) — an abstraction / cross-app held-out test.	togglelist exact 0.44 → 0.56	§35.7

🧱 Object-state / φ-expansion — CWM trace-format studies (§22–§25)

adapter	what it teaches	result	REPORT
`cwm_oop_expanded`	φ-expansion SFT teaching object-state observability (render object attributes each frame).	oop free-roll 0.02 → 0.93	§22–24
`cwm_mixed_expanded`	φ-expansion + mixed-corpus replay to eliminate catastrophic forgetting of a held-out long mode.	held-out multientity 0.68 → 1.0 (oop preserved)	§22
`cwm_dagger_gold`	OOP gold-prefix DAgger (matched 150-step budget).	free-roll 0.9324	§25
`cwm_dagger_drift`	OOP drift-prefix (on-policy) DAgger. Identical to gold → residual is a structural φ-render slip, not drift.	free-roll 0.9324	§25

➗ Arithmetic drift studies — mostly neutral (a capability hole, not fixable by this SFT) (§26–27)

adapter	what it teaches	result	REPORT
`cwm_arith_gold`	Correct-prefix (gold) per-frame SFT on long-arithmetic free-roll.	0.143 (fails — compounding value drift)	§26
`cwm_arith_drift`	Drift-prefix (single-round DAgger-style) SFT.	0.180 ≈ base	§26
`cwm_arith_wholetrace`	Whole-trace arithmetic SFT variant (same conclusion: needs tool-use/scratchpad, not SFT/RL).	≈ base	§26–27

Load an adapter (PEFT)

from peft import PeftModel
from transformers import AutoModelForCausalLM

base = AutoModelForCausalLM.from_pretrained("facebook/cwm", torch_dtype="auto", device_map="auto")
model = PeftModel.from_pretrained(
    base, "nmk-kun/cwm-extended-adapters",
    subfolder="cwm_gametick_stepover",   # <- any folder name from the tables above
)

For vLLM-based inference (the project's harness), see models/cwm_trace.py and the run_*.py probes in the GitHub repo; each adapter is loaded in its own process (vLLM 0.23 has a multi-adapter-per-session bug).

Which one do I want?

Game-tick prediction / the flywheel headline: cwm_gametick_stepover → cwm_fdm_idm_r1 → cwm_fdm_idm_r2.
UI/DOM render-FDM: cwm_cascade (but note the real-JS regression; base CWM is often the better UI FDM — see REPORT §34, §39).
Object-state observability: cwm_oop_expanded / cwm_mixed_expanded.
The oracle, dagger, and arith adapters are controls / ablations, not deployment targets.

License

Built on Meta FAIR's Code World Model; intended for noncommercial research use consistent with the FAIR Noncommercial Research License. See https://github.com/facebookresearch/cwm.

Library: peft
License: other
Created: Jun 30, 2026
Updated: Jun 30, 2026