Molmo-7B-D-0924
Maintainer: allenai
268
🤯
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model Overview
Molmo is a family of open vision-language models developed by the Allen Institute for AI. Molmo 7B-D is based on the Qwen2-7B model and uses the OpenAI CLIP vision backbone. It performs well on both academic benchmarks and human evaluation, falling between GPT-4V and GPT-4o in capability. This checkpoint is a preview of the upcoming Molmo model release.
Model Inputs and Outputs
Inputs
- Images: The model can process a single image at a time. The image can be passed as a PIL Image object.
- Text: The model takes a text prompt as input to describe the provided image.
Outputs
- Text: The model generates text describing the provided image. The output is a string of text.
Capabilities
Molmo 7B-D demonstrates strong multimodal capabilities, combining vision and language understanding to generate high-quality descriptive text for images. It outperforms many similar-sized models on benchmarks while remaining fully open-source.
What Can I Use It For?
The Molmo demo showcases the model's ability to generate detailed, contextual descriptions for a variety of images. This could be useful for applications like automated image captioning, visual question answering, and content creation. As an open-source model, Molmo 7B-D can also be fine-tuned for specialized use cases.
Things to Try
Try providing the model with different types of images - natural scenes, artistic works, diagrams, etc. - and observe how it describes the content and context. You can also experiment with prompting the model to generate captions with different levels of detail or creative flair.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
👀
Molmo-7B-O-0924
112
The Molmo-7B-O-0924 model is part of the Molmo family of open vision-language models developed by the Allen Institute for AI. Molmo models are trained on PixMo, a dataset of 1 million highly-curated image-text pairs. This model is based on the OLMo-7B-1124 model and uses the OpenAI CLIP as its vision backbone. It performs well on both academic benchmarks and human evaluation, falling between the capabilities of GPT-4V and GPT-4o. Model inputs and outputs Inputs Images**: The model can process images to be used as input for vision-language tasks. Text**: The model can take text as input for language-based tasks. Outputs Text**: The model can generate human-like text in response to prompts. Image Descriptions**: The model can generate descriptive text for images. Capabilities The Molmo-7B-O-0924 model has state-of-the-art performance among multimodal models of a similar size, while being fully open-source. It is capable of tasks such as image captioning, visual question answering, and multimodal reasoning. The model's strong performance on benchmarks like VQA and PIQA demonstrates its ability to understand and reason about images and language. What can I use it for? The Molmo-7B-O-0924 model can be used for a variety of vision-language tasks, such as powering image-based chatbots, visual question answering systems, and multimodal content generation. Its open-source nature and strong performance make it a valuable tool for researchers and developers working on multimodal AI applications. You can find a demo and all models in the Molmo family on the project's website. Things to try One interesting aspect of the Molmo-7B-O-0924 model is its ability to handle transparent images. While the model may struggle with this type of input, the maintainers provide a code snippet to add a white or dark background to the image before passing it to the model. This highlights the importance of understanding a model's limitations and using input preprocessing techniques to improve its performance on challenging data.
Updated Invalid Date
✨
Molmo-72B-0924
197
The Molmo-72B-0924 is a open vision-language model developed by the Allen Institute for AI. It is part of the Molmo family of models, which are trained on PixMo, a dataset of 1 million highly-curated image-text pairs. The Molmo-72B model is based on the Qwen2-72B model and uses the OpenAI CLIP model as its vision backbone. It achieves state-of-the-art performance on academic benchmarks and ranks second on human evaluation, just slightly behind GPT-4o. Model inputs and outputs Inputs Images Text descriptions Outputs Text generation based on the input image Capabilities The Molmo-72B model excels at multimodal tasks that involve both images and text. It can generate detailed and coherent text descriptions of images, going beyond simple captions. The model also demonstrates strong performance on various visual-language benchmarks, showcasing its ability to understand and reason about visual concepts. What can I use it for? The Molmo-72B model can be useful for a variety of applications, such as image captioning, visual question answering, and content generation. Businesses could leverage the model's capabilities to automate the creation of image descriptions for e-commerce or social media, or to generate personalized content for their customers. Researchers can also use the model as a starting point for further exploration and fine-tuning on specific tasks. Things to try One interesting aspect of the Molmo-72B model is its ability to combine visual and textual information in novel ways. For example, you could try using the model to generate creative story ideas or poetry inspired by a given image. The model's strong understanding of visual concepts and its language generation capabilities make it a versatile tool for exploring multimodal creativity.
Updated Invalid Date
🛸
MolmoE-1B-0924
85
MolmoE-1B-0924 is a multimodal Mixture-of-Experts LLM with 1.5B active and 7.2B total parameters, developed by the Allen Institute for AI. It is based on OLMoE-1B-7B-0924, and nearly matches the performance of GPT-4V on both academic benchmarks and human evaluation. MolmoE-1B-0924 achieves state-of-the-art performance among similarly-sized open multimodal models. The Molmo family of models are open vision-language models trained on the PixMo dataset, a collection of 1 million highly-curated image-text pairs. Molmo models demonstrate strong performance on a range of multimodal tasks while being fully open-source. The Molmo-7B-D-0924 and Molmo-7B-O-0924 models, for example, perform competitively with GPT-4V and GPT-4o on academic benchmarks and human evaluation. Model inputs and outputs Inputs Images**: The model can accept a single image or a batch of images as input. Text**: The model can also accept text prompts or questions related to the input images. Outputs Captions**: The model can generate captions that describe the contents of the input images. Answers**: The model can provide answers to questions about the input images. Capabilities MolmoE-1B-0924 demonstrates strong multimodal understanding and generation capabilities. It can accurately describe the contents of diverse images, answering questions about them and generating relevant text. For example, given an image of a puppy sitting on a wooden deck, the model could generate a caption like "This image features an adorable black Labrador puppy sitting on a weathered wooden deck." What can I use it for? MolmoE-1B-0924 can be useful for a variety of applications that require understanding and generating text related to visual inputs, such as: Image captioning**: Automatically generating descriptive captions for images. Visual question answering**: Answering questions about the contents of images. Multimodal dialogue**: Engaging in conversations that involve both text and images. Multimodal content creation**: Generating image-text pairs for tasks like content creation, education, and storytelling. Things to try One interesting aspect of MolmoE-1B-0924 is its ability to handle a diverse range of image types, including those with transparent backgrounds. While the model may struggle with some transparent images, you can use the provided code snippet to add a solid background to the image before passing it to the model, which can help improve the performance. Additionally, the model's Mixture-of-Experts architecture allows it to excel at a variety of multimodal tasks, so you may want to experiment with different prompts and image-text combinations to see the full extent of its capabilities.
Updated Invalid Date
✨
Molmo-72B-0924
197
The Molmo-72B-0924 is a open vision-language model developed by the Allen Institute for AI. It is part of the Molmo family of models, which are trained on PixMo, a dataset of 1 million highly-curated image-text pairs. The Molmo-72B model is based on the Qwen2-72B model and uses the OpenAI CLIP model as its vision backbone. It achieves state-of-the-art performance on academic benchmarks and ranks second on human evaluation, just slightly behind GPT-4o. Model inputs and outputs Inputs Images Text descriptions Outputs Text generation based on the input image Capabilities The Molmo-72B model excels at multimodal tasks that involve both images and text. It can generate detailed and coherent text descriptions of images, going beyond simple captions. The model also demonstrates strong performance on various visual-language benchmarks, showcasing its ability to understand and reason about visual concepts. What can I use it for? The Molmo-72B model can be useful for a variety of applications, such as image captioning, visual question answering, and content generation. Businesses could leverage the model's capabilities to automate the creation of image descriptions for e-commerce or social media, or to generate personalized content for their customers. Researchers can also use the model as a starting point for further exploration and fine-tuning on specific tasks. Things to try One interesting aspect of the Molmo-72B model is its ability to combine visual and textual information in novel ways. For example, you could try using the model to generate creative story ideas or poetry inspired by a given image. The model's strong understanding of visual concepts and its language generation capabilities make it a versatile tool for exploring multimodal creativity.
Updated Invalid Date