Molmo2 is a family of open vision-language models developed by the Allen Institute for AI (Ai2) that support image, video and multi-image understanding and grounding. Molmo2 models are trained on publicly available third party datasets as referenced in our technical report and Molmo2 data, a collection of datasets with highly-curated image-text and video-text pairs. It has state-of-the-art performance among multimodal models with a similar size. You can find all models in the Molmo2 family here. **Learn more** about the Molmo2 family in our announcement blog post. Molmo2-8B is based on Qwen3-8B and uses SigLIP 2 as vision backbone. It outperforms others in the class of open weight and data models on short videos, counting, and captioning, and is competitive on long-videos.
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**Molmo2-ER** (Embodied Reasoning) is a 4B vision–language model specialized for the embodied perception skills that downstream action models depend on: scene understanding, pixel-accurate pointing, multi-image and egocentric–exocentric correspondence, and video temporal reasoning. It is built on top of Molmo2 (Qwen3-4B backbone + SigLIP2 vision encoder) and serves as the vision–language backbone of the **MolmoAct2** action reasoning model. - **Outperforms every open-weight baseline** as well as the strongest closed-source models — including **Gemini Robot-ER 1.5 Thinking** and **GPT-5** — on **9 of 13** established embodied reasoning benchmarks (Point-Bench, RefSpatial, BLINK, CV-Bench, ERQA, EmbSpatial, MindCube, SAT, VSI-Bench). - **Overall average 63.8%**, a **+17 point** improvement over the Molmo2 starting point.
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Molmo2 is a family of open vision-language models developed by the Allen Institute for AI (Ai2) that support image, video and multi-image understanding and grounding. Molmo2 models are trained on publicly available third party datasets as referenced in our technical report and Molmo2 data, a collection of datasets with highly-curated image-text and video-text pairs. It has state-of-the-art performance among multimodal models with a similar size. You can find all models in the Molmo2 family here. **Learn more** about the Molmo2 family in our announcement blog post. Molmo2-O-7B is based on Olmo3-7B-Instruct and uses SigLIP 2 as vision backbone. It outperforms others in the class of open weight and data models on short videos, counting, and captioning, and is competitive on long-videos.
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MolmoAct2 is an open vision-language-action model for robot control. It builds on Molmo2-ER, an embodied-reasoning VLM backbone, and connects the autoregressive VLM to a flow-matching continuous action expert through per-layer KV (key-value) conditioning. This checkpoint is the post-trained, multi-embodiment MolmoAct2 model. It is intended as a foundation checkpoint for further robot fine-tuning rather than as a ready-to-run policy for a single deployment setting. Use this checkpoint for further fine-tuning on a target robot embodiment or benchmark. It contains the VLM and continuous action expert weights, plus normalization metadata for the post-training mixture in `norm_stats.json`.
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MolmoAct2 is an open vision-language-action model for robot control. It builds on Molmo2-ER and attaches a flow-matching continuous action expert that conditions on the VLM key-value cache through a per-layer connection. This checkpoint is fine-tuned on the full LIBERO training mixture, combining Spatial, Object, Goal, and Long suites. It is intended for both further fine-tuning and LIBERO policy inference. Use this checkpoint for LIBERO inference or for further fine-tuning. Dataset normalization metadata is stored in `norm_stats.json`. pass `norm_tag="libero"` at inference time.
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This MolmoAct2 checkpoint is the LeRobot version of MolmoAct2-LIBERO, fine-tuned and evaluated on LIBERO tasks. This checkpoint was fine-tuned from `allenai/MolmoAct2-LIBERO` for an additional 10k steps on `allenai/MolmoAct2-LIBERO-Dataset` with per-GPU batch size 32 on 8 H100 GPUs. **Original paper:** MolmoAct2: Action Reasoning Models for Real-world Deployment **Reference implementation:** https://github.com/allenai/molmoact2 **LeRobot policy:** `lerobot.policies.molmoact2`
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MolmoAct2 is an open vision-language-action model for robot control. It builds on Molmo2-ER and attaches a flow-matching continuous action expert that conditions on the VLM key-value cache through a per-layer connection. This checkpoint is fine-tuned on the SO-100/101 mixture with absolute joint-pose control and annotated language instructions. It is intended for both further fine-tuning and SO-100/101 policy inference. - 📂 Models: Models, Finetuned Models - 📂 Datasets: MolmoAct2-BimanualYAM Dataset, MolmoAct2 Datasets, Molmo2-ER Datasets - 📄 Paper: arXiv:2605.02881 - 💻 Code: allenai/molmoact2 - 🎥 Blog Post: MolmoAct2
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MolmoPoint-8B is a fully-open VLM developed by the Allen Institute for AI (Ai2) that support image, video and multi-image understanding and grounding. It has new pointing mechansim that improves image pointing, video pointing, and video tracking, see our technical report for details. Note the huggingface MolmoPoint model does not support training, see our github repo for the training code. We recommend running MolmoPoint with `logits_processor=model.build_logit_processor_from_inputs(model_inputs)` to enforce points tokens are generated in a valid way.
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OLMo 1B July 2024 is the latest version of the original OLMo 1B model rocking a 4.4 point increase in HellaSwag, among other evaluations improvements, from an improved version of the Dolma dataset and staged training. **This version is for direct use with HuggingFace Transformers** from v4.40 on. OLMo is a series of **O**pen **L**anguage **Mo**dels designed to enable the science of language models. The OLMo models are trained on the Dolma dataset. We release all code, checkpoints, logs, and details involved in training these models. The core models released in this batch are the following: | Size | Training Tokens | Layers | Hidden Size | Attention Heads | Context Length | |------|--------|---------|-------------|-----------------|----------------| | OLMo 1B July 2024 | 3.05 Trillion | 16 | 2048 | 16 | 4096 | | OLMo 7B July 2024 | 2.75 Trillion | 32 | 4096 | 32 | 4096 |
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