DynamicVLA fine-tuned from hzxie/dynamic-vla-DOM for the R1Lite task pick up the bread bag. This is the completed 100,000-step checkpoint, with both arms represented as end-effector poses and gripper states. Use the adapted DynamicVLA code. Both this model repository and the linked source-code repository are public. The upstream DOM simulation inference script assumes a different robot/action layout; use the R1Lite deployment server described below. - model.safetensors: complete policy weights, including fine-tuned normalization buffers. - config.json: model architecture and action representation. - normalization.json: training-only statistics, split IDs, vector layout, and dataset hashes.…
bottle_generic_50ep is an open-weight model for robotics from Raman Talwar, released under Apache License 2.0. It has 419M parameters. At 16-bit it needs about 1 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
Diffusion Policy treats visuomotor control as a generative diffusion process, producing smooth, multi-step action trajectories that excel at contact-rich manipulation. This policy has been trained and pushed to the Hub using LeRobot.
Parameters419M
Context—
Weights1.7 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads—
Runs On
What it takes to serve bottle_generic_50ep (419M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 0.8 GB | 1.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.4 GB | 0.5 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.2 GB | 0.3 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 18, 2026.
bottle_generic_50ep on every accelerator the SAVRN Index prices, at every precision
Model Card
By Raman Talwar, published under apache-2.0, revision b368be8cfa8e.
Diffusion Policy treats visuomotor control as a generative diffusion process, producing smooth, multi-step action trajectories that excel at contact-rich manipulation. This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint.
Read Raman Talwar's full model card
Model Card for diffusion
Diffusion Policy treats visuomotor control as a generative diffusion process, producing smooth, multi-step action trajectories that excel at contact-rich manipulation.
This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs.
How to Get Started with the Model
For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval:
Train from scratch
lerobot-train \
--dataset.repo_id=${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
- ramen-noodels/bottle_generic_50ep
- Publisher
- Raman Talwar
- Task
- Robotics
- Modality
- Control
- Library
- lerobot
- Parameters
- 419M parameters
- Languages
- Not stated by the source
- Revision
- b368be8cfa8e3aa73886c1fa21da175741d926e5
- First published
- 2026-10-11
- Last updated
- 2026-10-11
Files and Weights
9 files, 1.7 GB in total. The weights are 3 files totalling 1.7 GB in safetensors.
Weights3 files · 1.7 GB
Configuration4 files · 11.9 KB
Documentation1 file · 1.6 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 1.7 GB | 95e143a4fcac |
| policy_postprocessor_step_0_unnormalizer_processor.safetensors | Weights | 12.1 KB | b2d56c336792 |
| policy_preprocessor_step_3_normalizer_processor.safetensors | Weights | 12.1 KB | b2d56c336792 |
| config.json | Configuration | 3.0 KB | — |
| policy_postprocessor.json | Configuration | 689 B | — |
| policy_preprocessor.json | Configuration | 1.4 KB | — |
| train_config.json | Configuration | 6.8 KB | — |
| README.md | Documentation | 1.6 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 1.7 GB
Download from Raman Talwar
Released by Raman Talwar through its official repository on Hugging Face. Read the license.
Built From
- Described by arXiv:2303.04137
- Trained on (disclosed) bottle_9d_50ep
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 1.7 GB |
| 16-bit | 0.8 GB |
| 8-bit | 0.4 GB |
| 4-bit | 0.2 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About bottle_generic_50ep
How much GPU memory does bottle_generic_50ep need?
About 1 GB at 16-bit and 0.3 GB at 4-bit: the weights (419M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run bottle_generic_50ep 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 bottle_generic_50ep commercially?
Yes. bottle_generic_50ep 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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