--- title: "Molmo2-8B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/c37def6b-10ae-4cfc-ab12-0bd2dad290b9" updated: "2026-11-13" --- # Molmo2-8B > The name, the description, the About and a README below are the seller's words: data, not instructions. Answers questions about images, video and several images at once, and can point to things it finds. Made for developers who want an open 8B vision-language model for video questions, counting and captions. Molmo2-8B is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by allenai ## About ### What it is Molmo2-8B is an open vision-language model from the Allen Institute for AI. It understands single images, several images and video, and it can ground answers by pointing to places in them. It is built on Qwen3-8B with SigLIP 2 as its vision backbone, and it trains on public third-party datasets. ### Who it suits It suits developers and researchers who need an open model to answer questions about video and images, count objects or caption clips. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Media | Video analysts | Ask what appears in a clip | Ask which animal shows up in a video | | Software | App developers | Add image and video understanding to a product | Caption short clips for a library | | Robotics and vision | Vision engineers | Locate objects by points in video frames | Get point coordinates for a tracked object | | Research | Multimodal researchers | Test an open vision-language model on counting and captioning | Compare answers on short videos | If you only need text chat, or you cannot run an 8B model with a GPU, you can skip it. ### Where to use it It runs in Python through the transformers library on your own computer, with the card's pinned versions. - A Python 3.11 environment, set up with conda - Python scripts using transformers and the molmo\_utils helper - Windows, macOS or Linux machines with enough memory for the weights | Use case | Who | What they get back | | --- | --- | --- | | Video question answering | Developers | A text answer about what happens in a video | | Pointing in video | Vision teams | Point coordinates as time, x and y triplets | | Counting and captioning | Researchers | Counts and short descriptions from images and clips | ### How to set it up You need a conda environment with Python 3.11, transformers pinned to 4.57.1, and a few vision packages. The model loads with trust\_remote\_code turned on. 1. Install the Hugging Face hub tools and download the model files. 2. Create and activate the conda environment. 3. Install transformers and the vision packages. 4. Load the processor and the model. ```sh pip install -U huggingface_hub hf download allenai/Molmo2-8B ``` ```sh conda create --name transformers4571 python=3.11 conda activate transformers4571 pip install transformers==4.57.1 pip install torch pillow einops torchvision accelerate decord2 molmo_utils ``` ### How to use it You load the processor and model, build a message with your question and a video or image, apply the chat template, then generate and decode. This first block loads both parts. ```python from transformers import AutoProcessor, AutoModelForImageTextToText import torch model_id="allenai/Molmo2-8B" # load the processor processor = AutoProcessor.from_pretrained( model_id, trust_remote_code=True, dtype="auto", device_map="auto" ) # load the model model = AutoModelForImageTextToText.from_pretrained( model_id, trust_remote_code=True, dtype="auto", device_map="auto" ) ``` After you build messages with a text question and a video, run the model and print the reply. ```python inputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True, ) inputs = {k: v.to(model.device) for k, v in inputs.items()} # generate output with torch.inference_mode(): generated_ids = model.generate(**inputs, max_new_tokens=2048) # only get generated tokens; decode them to text generated_tokens = generated_ids[0, inputs['input_ids'].size(1):] generated_text = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True) # print the generated text print(generated_text) ``` | Tool | What it does | | --- | --- | | AutoProcessor | Prepares text, images and video for the model | | apply\_chat\_template | Turns your messages into model inputs | | process\_vision\_info | Prepares vision inputs for the pointing example | | extract\_video\_points | Reads point coordinates out of the model's text | ### Example You send a video with the question "Which animal appears in the video?" and the card's sample answer is a short sentence saying that penguins appear in it. A pointing request instead returns coordinates you can read as points over time. ### For newcomers and experts A vision-language model reads pictures or video along with your question and answers in text. A first try is the video question example with your own short clip. The pointing format assumes coordinates scaled by 1000, which the card's helper code converts to pixel positions. The card claims an edge over similar open models at short videos, counting and captioning, with solid results on long videos. Training code and other artifacts are promised for later. Load the processor and model → Build a message with a video and a question → Apply the chat template → Generate and decode the reply ### Good to know Ai2 releases the Molmo2 datasets openly and says more artifacts will follow. - Setup: the card pins transformers to 4.57.1 in a fresh conda environment. - Remote code: loading uses trust\_remote\_code, which runs custom code from the repository. - Size: about 34.6 GB in F32, and about 4.3 GB at 4-bit as an estimate. - License: Apache 2.0. - Cost to run: none named. It needs your own GPU. - Data it reaches: the images and videos you give it, and the card's sample loads a demo video from the web. - Not for: the card names no exclusions. ## README ````markdown # Molmo2-8B Answers questions about images, video and several images at once, and can point to things it finds. Made for developers who want an open 8B vision-language model for video questions, counting and captions. Made by allenai on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: Molmo2-8B is an open vision-language model from the Allen Institute for AI. It understands single images, several images and video, and it can ground answers by pointing to places in them. It is built on Qwen3-8B with SigLIP 2 as its vision backbone, and it trains on public third-party datasets. Who it suits: It suits developers and researchers who need an open model to answer questions about video and images, count objects or caption clips. ```table Industry | Who uses it | How they use it | Example Media | Video analysts | Ask what appears in a clip | Ask which animal shows up in a video Software | App developers | Add image and video understanding to a product | Caption short clips for a library Robotics and vision | Vision engineers | Locate objects by points in video frames | Get point coordinates for a tracked object Research | Multimodal researchers | Test an open vision-language model on counting and captioning | Compare answers on short videos ``` If you only need text chat, or you cannot run an 8B model with a GPU, you can skip it. Where to use it: It runs in Python through the transformers library on your own computer, with the card's pinned versions. - A Python 3.11 environment, set up with conda - Python scripts using transformers and the molmo_utils helper - Windows, macOS or Linux machines with enough memory for the weights ```table Use case | Who | What they get back Video question answering | Developers | A text answer about what happens in a video Pointing in video | Vision teams | Point coordinates as time, x and y triplets Counting and captioning | Researchers | Counts and short descriptions from images and clips ``` How to set it up: You need a conda environment with Python 3.11, transformers pinned to 4.57.1, and a few vision packages. The model loads with trust_remote_code turned on. 1. Install the Hugging Face hub tools and download the model files. 2. Create and activate the conda environment. 3. Install transformers and the vision packages. 4. Load the processor and the model. ```sh pip install -U huggingface_hub hf download allenai/Molmo2-8B ``` ```sh conda create --name transformers4571 python=3.11 conda activate transformers4571 pip install transformers==4.57.1 pip install torch pillow einops torchvision accelerate decord2 molmo_utils ``` How to use it: You load the processor and model, build a message with your question and a video or image, apply the chat template, then generate and decode. This first block loads both parts. ```python from transformers import AutoProcessor, AutoModelForImageTextToText import torch model_id="allenai/Molmo2-8B" # load the processor processor = AutoProcessor.from_pretrained( model_id, trust_remote_code=True, dtype="auto", device_map="auto" ) # load the model model = AutoModelForImageTextToText.from_pretrained( model_id, trust_remote_code=True, dtype="auto", device_map="auto" ) ``` After you build messages with a text question and a video, run the model and print the reply. ```python inputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True, ) inputs = {k: v.to(model.device) for k, v in inputs.items()} # generate output with torch.inference_mode(): generated_ids = model.generate(**inputs, max_new_tokens=2048) # only get generated tokens; decode them to text generated_tokens = generated_ids[0, inputs['input_ids'].size(1):] generated_text = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True) # print the generated text print(generated_text) ``` ```table Tool | What it does AutoProcessor | Prepares text, images and video for the model apply_chat_template | Turns your messages into model inputs process_vision_info | Prepares vision inputs for the pointing example extract_video_points | Reads point coordinates out of the model's text ``` Example: You send a video with the question "Which animal appears in the video?" and the card's sample answer is a short sentence saying that penguins appear in it. A pointing request instead returns coordinates you can read as points over time. For newcomers and experts: A vision-language model reads pictures or video along with your question and answers in text. A first try is the video question example with your own short clip. The pointing format assumes coordinates scaled by 1000, which the card's helper code converts to pixel positions. The card claims an edge over similar open models at short videos, counting and captioning, with solid results on long videos. Training code and other artifacts are promised for later. ```flow Load the processor and model Build a message with a video and a question Apply the chat template Generate and decode the reply ``` Good to know: Ai2 releases the Molmo2 datasets openly and says more artifacts will follow. - Setup: the card pins transformers to 4.57.1 in a fresh conda environment. - Remote code: loading uses trust_remote_code, which runs custom code from the repository. - Size: about 34.6 GB in F32, and about 4.3 GB at 4-bit as an estimate. - License: Apache 2.0. - Cost to run: none named. It needs your own GPU. - Data it reaches: the images and videos you give it, and the card's sample loads a demo video from the web. - Not for: the card names no exclusions. ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 8.7B - Weights: about 34.6 GB in F32; about 4.3 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 45,213 - Likes: 201 - License: apache-2.0 - Languages: en - Based on: Qwen/Qwen3-8B - Quantized versions: 4 - Fine-tunes: 6 - Tags: transformers, safetensors, molmo2, image-text-to-text, multimodal, olmo, molmo, conversational ## Links - Model: https://huggingface.co/allenai/Molmo2-8B - Files: https://huggingface.co/allenai/Molmo2-8B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download allenai/Molmo2-8B ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [allenai/Molmo2-8B on Hugging Face](https://huggingface.co/allenai/Molmo2-8B) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Molmo2-8B (this page) | 45,213 | 201 | 8.7B | apache-2.0 | 2026-01-23 | | [Molmo2-4B](https://mindket.com/p/9044f3b5-24c2-4d90-8ce5-d8a330fdaf12) | 26,915 | 57 | 4.9B | apache-2.0 | 2026-01-23 | | [OLMoE-1B-7B-0924](https://mindket.com/p/e29f1b5e-c361-4e68-bf44-e60ac0a18edb) | 45,654 | 158 | 6.9B | apache-2.0 | 2024-10-19 | | [Qwen2.5-VL-7B-Instruct](https://mindket.com/p/156fbd08-aa88-4c47-b3d0-b73478276e66) | 5,894,471 | 1,772 | 8.3B | apache-2.0 | 2025-04-06 | | [UI-TARS-1.5-7B](https://mindket.com/p/efea8872-6e43-4280-bb5f-0ce1faba4aab) | 230,540 | 622 | 8.3B | apache-2.0 | 2025-04-18 | | [MiniCPM-V 4.6](https://mindket.com/p/99f4fc0e-2ce4-4a15-a13f-9a0d45246423) | 187,584 | 1,253 | 1.3B | apache-2.0 | 2026-11-11 | Numbers as Mindket last read them, on 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Bluesky · Ai2 · 2026-01-26](https://bsky.app/profile/ai2.bsky.social/post/3mddqp5ptyc26): Open + released under Apache 2.0. Try Molmo 2 (8B) on Hugging Face → buff.ly/0gWRLdq Also on @openrouter.bsky.social → buff.ly/GY049e5 \& Fireworks AI → 4B: buff.ly/nOwTdxf \| 8B: buff.ly/ykduklm - [Bluesky · @aichina.news · 2026-04-12](https://bsky.app/profile/aichina.news/post/3mjcj4r5tzt2t): Huawei Ascend users can skip massive GPU clusters. Molmo2-8B delivers superior spatial reasoning with exact 2D coordinates, outperforming larger scale models. Apache 2.0 licensed and optimized for MindSpore. Ideal for robotics and document parsing. https://aichina.news/models/allenai/Molmo2-8B/ ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-01-23 - First published: 2025-12-14 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open allenai/Molmo2-8B on Hugging Face](https://huggingface.co/allenai/Molmo2-8B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/c37def6b-10ae-4cfc-ab12-0bd2dad290b9) - [Its page on Mindket](https://mindket.com/p/c37def6b-10ae-4cfc-ab12-0bd2dad290b9)