diamond

lucid-dl/diamond

diamond is a reinforcement learning model from lucid-dl. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product. It is set up for lucid. The card lists the license as mit.

Downloads · 30 days

Access

Public

Updated Aug 31, 2026

Repo size

2.9 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.safetensors2.9 GB · 100%

At a glance

Task: Reinforcement Learning
Library: lucid
License: mit
Access: Public
Created: Aug 31, 2026
Updated: Aug 31, 2026
SHA: 1f3d75ba

Datasets

atari-100k

Model card

From the Hugging Face model README

DIAMOND (CSGO)

https://arxiv.org/abs/2405.12399

Lucid port of https://huggingface.co/eloialonso/diamond, converted to Lucid-native safetensors.

Available weights

Tag	Params	GFLOPs	Size	Source
`Alien` (default)	0.0M	—	51.72 MB	eloialonso/diamond
`Amidar`	0.0M	—	51.7 MB	eloialonso/diamond
`Assault`	0.0M	—	51.69 MB	eloialonso/diamond
`Asterix`	0.0M	—	51.69 MB	eloialonso/diamond
`BankHeist`	0.0M	—	51.72 MB	eloialonso/diamond
`BattleZone`	0.0M	—	51.72 MB	eloialonso/diamond
`Boxing`	0.0M	—	51.72 MB	eloialonso/diamond
`Breakout`	0.0M	—	51.68 MB	eloialonso/diamond
`CSGO`	0.0M	—	1455.92 MB	eloialonso/diamond
`ChopperCommand`	0.0M	—	51.72 MB	eloialonso/diamond
`CrazyClimber`	0.0M	—	51.69 MB	eloialonso/diamond
`DemonAttack`	0.0M	—	51.69 MB	eloialonso/diamond
`Freeway`	0.0M	—	51.68 MB	eloialonso/diamond
`Frostbite`	0.0M	—	51.72 MB	eloialonso/diamond
`Gopher`	0.0M	—	51.69 MB	eloialonso/diamond
`Hero`	0.0M	—	51.72 MB	eloialonso/diamond
`Jamesbond`	0.0M	—	51.72 MB	eloialonso/diamond
`Kangaroo`	0.0M	—	51.72 MB	eloialonso/diamond
`Krull`	0.0M	—	51.72 MB	eloialonso/diamond
`KungFuMaster`	0.0M	—	51.71 MB	eloialonso/diamond
`MsPacman`	0.0M	—	51.69 MB	eloialonso/diamond
`Pong`	0.0M	—	51.69 MB	eloialonso/diamond
`PrivateEye`	0.0M	—	51.72 MB	eloialonso/diamond
`Qbert`	0.0M	—	51.69 MB	eloialonso/diamond
`RoadRunner`	0.0M	—	51.72 MB	eloialonso/diamond
`Seaquest`	0.0M	—	51.72 MB	eloialonso/diamond
`UpNDown`	0.0M	—	51.69 MB	eloialonso/diamond

Usage

import lucid.models as models
from lucid.models.weights import DiamondWeights

# default tag
model = models.diamond_csgo(pretrained=True)

# explicit tag (enum or string)
model = models.diamond_csgo(weights=DiamondWeights.CSGO)
model = models.diamond_csgo(pretrained="CSGO")

# preprocessing travels with the weights
weights = DiamondWeights.CSGO
preprocess = weights.transforms()
out = model(preprocess(image)[None])
logits = out.logits  # (B, num_classes)

Conversion

Converted from https://huggingface.co/eloialonso/diamond via python -m tools.convert_weights diamond --tag CSGO. Key mapping + numerical parity verified against the source.

License

mit — inherited from the original weights.

Citation

@inproceedings{alonso2024diffusion,
  title={Diffusion for World Modeling: Visual Details Matter in Atari},
  author={Alonso, Eloi and Jelley, Adam and Micheli, Vincent and Kanervisto, Anssi and Storkey, Amos and Pearce, Tim and Fleuret, Fran{\c{c}}ois},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2024}
}

Task: Reinforcement Learning
Library: lucid
License: mit
Created: Aug 31, 2026
Updated: Aug 31, 2026