MolmoAct-7B-O
Allen Institute for AI, USA · August 2025
Allen Institute for AI (Ai2)
- Parameters
- 7.7 B
- The allenai/MolmoAct-7B-O-0812 checkpoint reports 7,656,630,272 parameters in its safetensors index (Hugging Face model API,...
- GPU memory
- 14 GB
- weights at bf16, computed. Fits a 24 GB card
- Inference latency
- not published
- per action step
- Weights
- Apache-2.0
- downloadable checkpoint
What it is
MolmoAct-7B-O is the maximally open variant of MolmoAct, released alongside MolmoAct-7B-D on the same date. It swaps the Qwen2.5-7B and SigLIP2 stack for an OLMo-2-1124-7B language model with an OpenAI CLIP ViT-L/14-336 vision encoder, so that the language model itself is open weights, open data and open code rather than a third-party release. The paper describes it as its most open model and notes the caveat that the CLIP vision encoder is not itself fully open. Functionally it is the same Action Reasoning Model: depth perception tokens, editable trajectory traces, then discrete action tokens. The important limitation for anyone comparing models is that the paper reports 7B-O only in the architecture and hyperparameter appendices, and publishes no benchmark results for it, so its accuracy relative to 7B-D is undocumented.
Architecture
- Backbone
- OLMo-2-1124-7B language model with OpenAI CLIP ViT-L/14-336 vision encoder
- Action head
- Autoregressive discrete action tokens, same three-stage Action Reasoning Model pipeline as MolmoAct-7B-D (depth perception tokens, editable trajectory traces, low-level actions), 256 action bins.
- Action chunk
- 8 steps per forward pass
- Parameters
- The allenai/MolmoAct-7B-O-0812 checkpoint reports 7,656,630,272 parameters in its safetensors index (Hugging Face model API, checked 11.08.2026). Base models declared on the card: allenai/OLMo-2-1124-7B and openai/clip-vit-large-patch14-336.
- Pretraining data
- Same MolmoAct Pre-training Mixture, MolmoAct Dataset mid-training and Open X-Embodiment subset as MolmoAct-7B-D. The paper documents separate hyperparameter columns for 7B-D and 7B-O across pre-training, mid-training and post-training.
- Embodiments
- Not separately documented; the MolmoAct evaluation embodiments (Franka Emika Panda, Google Robot in SimplerEnv, LIBERO) are reported for 7B-D only
What hardware it needs
This is the section most people came for, so it is worth being precise about which numbers are measured and which are arithmetic.
| Precision | Weights | Note |
|---|---|---|
| fp32 | 29 GB | Training master weights, rarely used for inference |
| bf16 or fp16 | 14 GB | The usual way these checkpoints are served |
| int8 | 7.1 GB | Quantised, expect some loss of precision on fine motions |
| int4 | 3.6 GB | Aggressive quantisation, verify success rate before trusting it |
Smallest real card that fits the bf16 weights plus a 40 percent runtime allowance: 24 GB. That is an estimate for inference, not for fine tuning. Full fine tuning also holds optimiser state and gradients, which typically costs several times the weights unless you use LoRA or a comparable adapter.
Not published separately from MolmoAct-7B-D. The paper notes a tokenizer detail specific to 7B-O: because OLMo-2-7B has fewer than 130 padding tokens, the tokenizer and lm head must be padded first.
The authors publish no memory or latency figure for this model. Everything above is computed from the parameter count.
Reported results
Grouped by what the evaluation actually asked. Values from different suites are not comparable with each other, so each one keeps its suite and split.
Simulation
No results in this category are published for this model.
Real world
No results in this category are published for this model.
Fine tuned tasks
No results in this category are published for this model.
On real hardware
No real-world results are published for this variant. All real-world numbers in the MolmoAct paper refer to MolmoAct-7B-D.
Fine tuning it yourself
Same pipeline as MolmoAct-7B-D: LeRobot-format datasets preprocessed through the action reasoning pipeline, action chunk size 8, 256 action bins. The repository recommends MolmoAct-7B-D-0812 rather than 7B-O as the checkpoint for fine-tuning on custom datasets.
Where it helps, where it does not
Strengths
- Most open variant of MolmoAct: the language model (OLMo-2) is open weights, open data and open code, not just open weights
- Apache-2.0, so usable commercially
- Useful for reproducibility research where the full data provenance of the language backbone matters
Limits
- No benchmark results are published for this variant anywhere; the paper mentions 7B-O only in architecture and hyperparameter tables
- Openness is not complete: the OpenAI CLIP ViT-L/14-336 vision encoder is not fully open, which the paper acknowledges
- Not the recommended fine-tuning starting point; the repository points users to MolmoAct-7B-D-0812
- Inherits the MolmoAct generation's slow inference and is superseded by MolmoAct2 (May 2026)
- No latency, VRAM or control frequency figures
Sources
Everything on this page was taken from these documents. Where they disagree with what you read here, they win.
- https://arxiv.org/abs/2508.07917
- https://arxiv.org/pdf/2508.07917v4
- https://huggingface.co/allenai/MolmoAct-7B-O-0812
- https://github.com/allenai/molmoact
- https://allenai.org/blog/molmoact
Entry last checked 2026-08-11.
Try a policy on a real arm
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