Release physically pruned AutoPruner OFT Object checkpoint

a7cb152 verified 1 day ago

2.31 kB

---
base_model: moojink/openvla-7b-oft-finetuned-libero-object
pipeline_tag: robotics
library_name: transformers
tags:
- autopruner
- libero
- depth-pruning
---
# AutoPruner OFT Object: four retained decoder layers

This physical checkpoint removes original zero-based decoder layers 2–29 from
OpenVLA-OFT Object and retains layers 0, 1, 30 and 31. The selected recovery saves
step 10000, an MLP boundary adapter with bottleneck 1024, action head and proprio
projector. The source is the artifact path used by the paper's Object evaluation.

[Paper](https://arxiv.org/abs/2610.23880) · [Code and installation](https://github.com/123pyLeo/AutoPruner). The paper reports 463/500 successes.
That historical score is not a fresh reproduction. Preparation on 2026-10-30
verified two native action inputs against the source recovered policy with
maximum error 0.0; ten Object tasks with one standard initial-state trial each
completed at 9/10 under OSMesa. Those ten trials are a bounded check and are not
an independent final benchmark or a replacement for the paper's 500 episodes.

## Use

Follow the [installation guide](https://github.com/123pyLeo/AutoPruner/blob/main/docs/NEW_SERVER.md)
to install the pinned OFT environment and complete LIBERO simulation assets.
Download all files from this repository together, then run:

```bash
python -m autoprune.eval_oft_export --exported /path/to/this/model \
  --suite libero_object --trials 1 --max-eval-tasks 1 --run-root runs
```

No original search directory, dense reference, training data, teacher cache or
LLM API is needed for this evaluation. Do not prune this checkpoint a second
time. The command reads deletion indices and recovery components from
`autoprune_export.json`. Keep all tokenizer, modeling, normalization and recovery
files together. This checkpoint is for LIBERO Object simulation; no real-robot
or cross-embodiment validation is claimed.

## Sources and notices

Base model: [moojink/openvla-7b-oft-finetuned-libero-object](https://huggingface.co/moojink/openvla-7b-oft-finetuned-libero-object).
The upstream model card currently labels its license MIT. Retain applicable base
model and transitive component terms as well as the accompanying upstream and
framework notices. The code license does not relicense unrelated external assets.

	---
	base_model: moojink/openvla-7b-oft-finetuned-libero-object
	pipeline_tag: robotics
	library_name: transformers
	tags:
	- autopruner
	- libero
	- depth-pruning
	---
	# AutoPruner OFT Object: four retained decoder layers

	This physical checkpoint removes original zero-based decoder layers 2–29 from
	OpenVLA-OFT Object and retains layers 0, 1, 30 and 31. The selected recovery saves
	step 10000, an MLP boundary adapter with bottleneck 1024, action head and proprio
	projector. The source is the artifact path used by the paper's Object evaluation.

	[Paper](https://arxiv.org/abs/2610.23880) · [Code and installation](https://github.com/123pyLeo/AutoPruner). The paper reports 463/500 successes.
	That historical score is not a fresh reproduction. Preparation on 2026-10-30
	verified two native action inputs against the source recovered policy with
	maximum error 0.0; ten Object tasks with one standard initial-state trial each
	completed at 9/10 under OSMesa. Those ten trials are a bounded check and are not
	an independent final benchmark or a replacement for the paper's 500 episodes.

	## Use

	Follow the [installation guide](https://github.com/123pyLeo/AutoPruner/blob/main/docs/NEW_SERVER.md)
	to install the pinned OFT environment and complete LIBERO simulation assets.
	Download all files from this repository together, then run:

	```bash
	python -m autoprune.eval_oft_export --exported /path/to/this/model \
	--suite libero_object --trials 1 --max-eval-tasks 1 --run-root runs
	```

	No original search directory, dense reference, training data, teacher cache or
	LLM API is needed for this evaluation. Do not prune this checkpoint a second
	time. The command reads deletion indices and recovery components from
	`autoprune_export.json`. Keep all tokenizer, modeling, normalization and recovery
	files together. This checkpoint is for LIBERO Object simulation; no real-robot
	or cross-embodiment validation is claimed.

	## Sources and notices

	Base model: [moojink/openvla-7b-oft-finetuned-libero-object](https://huggingface.co/moojink/openvla-7b-oft-finetuned-libero-object).
	The upstream model card currently labels its license MIT. Retain applicable base
	model and transitive component terms as well as the accompanying upstream and
	framework notices. The code license does not relicense unrelated external assets.