Text / LLM / Model
Molmo2
- Organisation
- Allen Institute for AI (Ai2)
- Family
- Molmo · version 2
- Released
- 2025-12-11 (Ai2 blog post 'Molmo 2' dated December 11, 2025 (https://allenai.org/blog/molmo2); arXiv v1 2026-01-15)
- Checkpoints
- allenai/Molmo2-4B, allenai/Molmo2-8B, allenai/Molmo2-O-7B
- Weights
- Open · Apache 2.0
- Links
- PaperModel cardCode
Training data disclosure: Partial list
Level determined for 18% of benchmark cells.
- TestTest data seen0
- SeenSeen, split unclear0
- TrainTraining data seen3
- Related data seen1
- NoNot seen0
- ?Unknown18
“We train on the Molmo2 data, image data from PixMo, and various open-source datasets.”
“We create nine new datasets (in pink) to train Molmo2.”
Built on
Everything these base models may have seen is inherited by Molmo2.
-
Qwen3
· disclosure: Vague
Molmo2-4B and Molmo2-8B (model card: Qwen/Qwen3-8B). Molmo2-O-7B uses OLMo (see training_data).
Evidence
“We release three versions of Molmo2: 4B and 8B models based on the Qwen3 LLMs [169], and a 7B model based on the OLMo LLM [112]”
arxiv.org · paper · Section 1 · retrieved 2026-10-15 -
SigLIP 2
· disclosure: Partial list
Model card text says SigLIP 2 but its link and base_model metadata point to google/siglip-so400m-patch14-384 (SigLIP 1); see open_questions.
Evidence
“All models use the SigLIP 2 So400m/14 384px ViT [139].”
arxiv.org · paper · Appendix, Hyperparameters · retrieved 2026-10-15
Other declared training data
-
pre-training data of base model OLMo 3 7B (Molmo2-O-7B only) (broad web data: may contain benchmark test sets) · split: unknown
OLMo 3 is not in the allowed base-model id list for this pass, although olmo-3 exists in the registry.
Evidence
“Even with OLMo 3 as the LLM, our models still utilize a closed-data SigLIP 2 image”
arxiv.org · paper · Limitations · retrieved 2026-10-15 -
PixMo-Cap (captioning pre-training) (not yet matched to a dataset) · split: unknown
Evidence
“Our pre-training stage includes dense captioning with length conditioning and transcript prediction using PixMo-Cap”
arxiv.org · paper · Section 3.2 Pre-training · retrieved 2026-10-15 -
Tulu SFT data (English, no code) mixed into pre-training (not yet matched to a dataset) · split: unknown
Tulu version not stated here.
Evidence
“We add NLP data using the supervised fine-tuning data from Tulu [71], filtered to remove non-English content and code”
arxiv.org · paper · Section 3.2 Pre-training · retrieved 2026-10-15 -
PixMo-Points, PixMo-Count, CoSyn-Point (pointing pre-training) (not yet matched to a dataset) · split: unknown
Evidence
“Additionally, we add pointing data from PixMo-Points, PixMo-Count, and CoSyn-Point [172].”
arxiv.org · paper · Section 3.2 Pre-training · retrieved 2026-10-15 -
SFT mixture: PixMo, nine new Molmo2 datasets, Tulu, open-source video and image datasets (not yet matched to a dataset) · split: unknown · fine-tuning
Evidence
“Our data mixture combines PixMo [29], the Molmo2 datasets, Tulu, and other open-source video and image datasets.”
arxiv.org · paper · Section 3.2 SFT · retrieved 2026-10-15 -
ChartQA · split: train of llm-multimodal · fine-tuning
Reference [102] in the Image QA list is ChartQA. Split from the released SFT launch script (data loader split='train'); the paper itself only says 'following Molmo', and Molmo trained only on train sets.
Evidence
“open-source image datasets [48, 130, 101, 102, 103, 105, 65, 125, 94, 95, 14, 2, 61, 62, 107] following Molmo with CoSyn [172] instead of PixMo-Docs”
arxiv.org · paper · Table 1 (Image QA group) · retrieved 2026-10-15 “"chart_qa_weighted",”
raw.githubusercontent.com · code · IMAGE_ACADEMIC_DATASETS · retrieved 2026-10-15 “split="train",”
raw.githubusercontent.com · code · launch_scripts/sft.py, DataLoaderConfig · retrieved 2026-10-15 -
DocVQA · split: train of llm-multimodal · fine-tuning
Reference [103] is DocVQA. Split from the released SFT launch script.
Evidence
“open-source image datasets [48, 130, 101, 102, 103, 105, 65, 125, 94, 95, 14, 2, 61, 62, 107] following Molmo with CoSyn [172] instead of PixMo-Docs”
arxiv.org · paper · Table 1 (Image QA group) · retrieved 2026-10-15 “"doc_qa",”
raw.githubusercontent.com · code · IMAGE_ACADEMIC_DATASETS · retrieved 2026-10-15 “split="train",”
raw.githubusercontent.com · code · launch_scripts/sft.py, DataLoaderConfig · retrieved 2026-10-15 -
AI2D (AI2 Diagrams) · split: train of llm-multimodal · fine-tuning
Reference [65] is AI2D. Split from the released SFT launch script.
Evidence
“open-source image datasets [48, 130, 101, 102, 103, 105, 65, 125, 94, 95, 14, 2, 61, 62, 107] following Molmo with CoSyn [172] instead of PixMo-Docs”
arxiv.org · paper · Table 1 (Image QA group) · retrieved 2026-10-15 “"ai2_diagram_v2_mix_transparent",”
raw.githubusercontent.com · code · IMAGE_ACADEMIC_DATASETS · retrieved 2026-10-15 “split="train",”
raw.githubusercontent.com · code · launch_scripts/sft.py, DataLoaderConfig · retrieved 2026-10-15
Benchmark cells
Classic LLM benchmarks
Unknown MMLU (Measuring Massive Multitask Language Understanding) · unknown for its base model Qwen3: training data not disclosed in enough detail
Unknown GSM8K (Grade School Math 8K) · unknown for its base model Qwen3: training data not disclosed in enough detail
Unknown MATH (Mathematics Aptitude Test of Heuristics) · unknown for its base model Qwen3: training data not disclosed in enough detail
Unknown HumanEval · unknown for its base model Qwen3: training data not disclosed in enough detail
Unknown ARC-Challenge (AI2 Reasoning Challenge, Challenge Set) · unknown for its base model Qwen3: training data not disclosed in enough detail
Unknown HellaSwag · unknown for its base model Qwen3: training data not disclosed in enough detail
Unknown TruthfulQA · unknown for its base model Qwen3: training data not disclosed in enough detail
Unknown WinoGrande · unknown for its base model Qwen3: training data not disclosed in enough detail
Multimodal LLM benchmarks
Unknown MMMU (Massive Multi-discipline Multimodal Understanding and Reasoning) · unknown for its base model Qwen3: training data not disclosed in enough detail
Unknown MMMU-Pro · unknown for its base model Qwen3: training data not disclosed in enough detail
MathVista · in fine-tuning data: related dataset ChartQA was used
Evidence (6)
“open-source image datasets [48, 130, 101, 102, 103, 105, 65, 125, 94, 95, 14, 2, 61, 62, 107] following Molmo with CoSyn [172] instead of PixMo-Docs”
“"chart_qa_weighted",”
“split="train",”
“It consists of 6,141 examples, derived from 28 existing multimodal datasets involving mathematics and 3 newly created datasets”
“ChartQA (2022)”
“"dataset": "ChartQA",”
Training data seen ChartQA · in fine-tuning data: only the training split was used, not the benchmark's test data
Evidence (3)
“open-source image datasets [48, 130, 101, 102, 103, 105, 65, 125, 94, 95, 14, 2, 61, 62, 107] following Molmo with CoSyn [172] instead of PixMo-Docs”
“"chart_qa_weighted",”
“split="train",”
Training data seen DocVQA · in fine-tuning data: only the training split was used, not the benchmark's test data
Evidence (3)
“open-source image datasets [48, 130, 101, 102, 103, 105, 65, 125, 94, 95, 14, 2, 61, 62, 107] following Molmo with CoSyn [172] instead of PixMo-Docs”
“"doc_qa",”
“split="train",”
Training data seen AI2D (AI2 Diagrams) · in fine-tuning data: only the training split was used, not the benchmark's test data
Evidence (3)
“open-source image datasets [48, 130, 101, 102, 103, 105, 65, 125, 94, 95, 14, 2, 61, 62, 107] following Molmo with CoSyn [172] instead of PixMo-Docs”
“"ai2_diagram_v2_mix_transparent",”
“split="train",”
Unknown CharXiv · unknown for its base model Qwen3: training data not disclosed in enough detail
Recent LLM benchmarks
Unknown GPQA Diamond · unknown for its base model Qwen3: training data not disclosed in enough detail
Unknown MMLU-Pro · unknown for its base model Qwen3: training data not disclosed in enough detail
Unknown LiveCodeBench (code generation, release_v6) · unknown for its base model Qwen3: training data not disclosed in enough detail
Unknown SWE-bench Verified · unknown for its base model Qwen3: training data not disclosed in enough detail
Unknown AIME 2024 · unknown for its base model Qwen3: training data not disclosed in enough detail
Unknown AIME 2025 · unknown for its base model Qwen3: training data not disclosed in enough detail
Unknown MATH-500 · unknown for its base model Qwen3: training data not disclosed in enough detail
Notes
Paper notes Molmo2 lags on reasoning benchmarks (MathVista, MMMU), 'possibly due to the lack of multi-modal reasoning training data'. A decontamination step is reported only for MOSE videos against the MeViS validation set (video tracking), not for the multimodal benchmarks here. Accepts image input: “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.”
Sources
- Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding (arXiv 2601.10611v4) · paper · retrieved 2026-10-15
- Molmo2-8B model card · model card · retrieved 2026-10-15
- allenai/molmo2 launch_scripts/sft.py · code · retrieved 2026-10-15
- Ai2 blog: Molmo 2 (2025-12-11; codebase released 3/3) · website · retrieved 2026-10-15