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knightnemo/
nanowm-b2-dino-wm-wall-15k

Name: nanowm-b2-dino-wm-wall-15k
Availability: InStock

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NanoWM-B/2 · DINO-WM / wall

Phase-2 baseline for the wall environment from the DINO-WM suite, trained with the best ablation config (pred_target=v, additive action injection, cosine + ZTSNR) on NanoWM-B/2 for 15,000 steps.

Run identity

wandb: https://wandb.ai/better_guidance/nano-world-model-phase2/runs/shk5u9eg
launcher: src/scripts/phase2/dino_wm_wall.sh
collection: https://huggingface.co/collections/knightnemo/nano-world-model

Training setup

Key	Value
Architecture	NanoWM-B/2 (~158.6M params)
Dataset	DINO-WM `wall` (osf.io/bmw48)
Frames × resolution	4 × 224² (DINO latent space)
Context frames	1
Action injection	additive
Steps	15,000
Batch	8/GPU × 8 × H20
Optimizer	AdamW, lr 1e-4, wd 0.01
Precision	bf16-mixed, `torch.compile` on
Seed	3407

Diffusion setup

Key	Value
pred_name	v
noise_schedule	`squaredcos_cap_v2` (cosine)
zero_terminal_snr	true
timestep_sampling	logit_normal
snr_gamma	5.0
diffusion_steps	1000 train · 250 DDIM sample

Loading

bash

git clone git@github.com:knightnemo/nano-world-model.git
cd nano-world-model
huggingface-cli download knightnemo/nanowm-b2-dino-wm-wall-15k --local-dir ./ckpt

python

import sys
from omegaconf import OmegaConf
from safetensors.torch import load_file
sys.path.insert(0, "src")
from models import get_models

cfg = OmegaConf.load("ckpt/config.yaml")
cfg.experiment.infra.compile = False
model = get_models(cfg).eval()

state_dict = load_file("ckpt/model.safetensors")
model.load_state_dict(state_dict, strict=True)