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Open-weight model · Robotics

select_block_dit_fdp3_4096

by Testing escapebirdy/select_block_dit_fdp3_4096

This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide.

Parameters18M
Context
Weights70.7 MB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads

Runs On

What it takes to serve select_block_dit_fdp3_4096 (18M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 0.0 GB 0.0 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 0.0 GB 0.0 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 0.0 GB 0.0 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.

Model Card

By Testing, published under apache-2.0, revision 4524e03298fb.

This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint.

Read Testing's full model card

Model Card for multi_task_dit

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This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs.


How to Get Started with the Model

For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval:

Train from scratch

lerobot-train \
  --dataset.repo_id=${HF_USER}/<dataset> \
  --policy.type=act \
  --output_dir=outputs/train/<desired_policy_repo_id> \
  --job_name=lerobot_training \
  --policy.device=cuda \
  --policy.repo_id=${HF_USER}/<desired_policy_repo_id>
  --wandb.enable=true

Writes checkpoints to outputs/train/<desired_policy_repo_id>/checkpoints/.

Evaluate the policy/run inference

lerobot-record \
  --robot.type=so100_follower \
  --dataset.repo_id=<hf_user>/eval_<dataset> \
  --policy.path=<hf_user>/<desired_policy_repo_id> \
  --episodes=10

Prefix the dataset repo with eval_ and supply --policy.path pointing to a local or hub checkpoint.


Model Details

  • License: apache-2.0

Identity and Version

Repository
escapebirdy/select_block_dit_fdp3_4096
Publisher
Testing
Task
Robotics
Modality
Control
Library
lerobot
Parameters
18M parameters
Languages
Not stated by the source
Revision
4524e03298fb4c82baa4a5370868f933309b33a6
First published
2026-09-18
Last updated
2026-09-18

Files and Weights

9 files, 70.7 MB in total. The weights are 3 files totalling 70.7 MB in safetensors.

Weights3 files · 70.7 MB
Configuration4 files · 12.0 KB
Documentation1 file · 1.5 KB
Repository1 file · 1.5 KB
Every file
FileTypeSizeSHA-256
model.safetensorsWeights70.7 MB 58d617c06f12
policy_postprocessor_step_0_unnormalizer_processor.safetensorsWeights6.4 KB 5404cdf9368b
policy_preprocessor_step_3_normalizer_processor.safetensorsWeights6.4 KB 5404cdf9368b
config.jsonConfiguration3.0 KB
policy_postprocessor.jsonConfiguration674 B
policy_preprocessor.jsonConfiguration1.0 KB
train_config.jsonConfiguration7.3 KB
README.mdDocumentation1.5 KB
.gitattributesRepository1.5 KB

License and Download

License
apache-2.0
Access
Open weights, no gate
Download size
70.7 MB
Download from Testing

Released by Testing through its official repository on Hugging Face. Read the license.

Built From

  • Trained on (disclosed) escapebirdy/select_block_4096_v3

Memory Requirements

PrecisionWeights in memory
As published70.7 MB
16-bit0.0 GB
8-bit0.0 GB
4-bit0.0 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About select_block_dit_fdp3_4096

How much GPU memory does select_block_dit_fdp3_4096 need?

About 0 GB at 16-bit and 0 GB at 4-bit: the weights (18M parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run select_block_dit_fdp3_4096 on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use select_block_dit_fdp3_4096 commercially?

Yes. select_block_dit_fdp3_4096 is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

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This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint.

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This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint.

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