Robotics
Transformers
Safetensors
English
molmoact2
image-text-to-text
OpenRAL
rskill
vision-language-action
nf4
4-bit precision
so100_follower
so101_follower
vla
so101
so100
manipulation
custom_code
8-bit precision
Instructions to use OpenRAL/rskill-molmoact2-multi-so101-nf4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenRAL/rskill-molmoact2-multi-so101-nf4 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForImageTextToText model = AutoModelForImageTextToText.from_pretrained("OpenRAL/rskill-molmoact2-multi-so101-nf4", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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docs: HF model card for OpenRAL/rskill-molmoact2-multi-so101-nf4 v0.1.0
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chore: publish rSkill OpenRAL/rskill-molmoact2-multi-so101-nf4 v0.1.0
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chore: canonical naming migration
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chore: canonical naming migration
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docs: HF model card — best-of-both front-matter derived from manifest
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SKILL.md: regenerate discovery view
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rskill.yaml: sync to OpenRAL namespace + current schema (joint_units, evaluated_tasks, ADR-0071)
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docs: add generated SKILL.md discovery view
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Duplicate from AdrianLlopart/rskill-molmoact2-so101-nf4
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