OLMoE-1B-7B-0924
Maintainer: allenai
100
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| 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 |
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Model overview
The OLMoE-1B-7B-0924 is a Mixture-of-Experts (MoE) language model developed by allenai. It has 1 billion active parameters and 7 billion total parameters, and was released in September 2024. The model yields state-of-the-art performance among models with a similar cost (1B) and is competitive with much larger models like Llama2-13B. OLMoE is 100% open-source.
Similar models include the OLMo-7B-0424 from allenai, which is a 7 billion parameter version of the OLM model released in April 2024. There is also the OLMo-Bitnet-1B from NousResearch, which is a 1 billion parameter model trained using 1-bit techniques.
Model inputs and outputs
Inputs
- Raw text to be processed by the language model
Outputs
- Continued text generation based on the input prompt
- Embeddings or representations of the input text that can be used for downstream tasks
Capabilities
The OLMoE-1B-7B-0924 model is capable of generating coherent and contextual text continuations, answering questions, and performing other natural language understanding and generation tasks. For example, given the prompt "Bitcoin is", the model can generate relevant text continuing the sentence, such as "Bitcoin is a digital currency that is created and held electronically. No one controls it. Bitcoins arent printed, like dollars or euros theyre produced by people and businesses running computers all around the world, using software that solves mathematical".
What can I use it for?
The OLMoE-1B-7B-0924 model can be used for a variety of natural language processing applications, such as text generation, dialogue systems, summarization, and knowledge-based question answering. For companies, the model could be fine-tuned and deployed in customer service chatbots, content creation tools, or intelligent search and recommendation systems. Researchers could also use the model as a starting point for further fine-tuning and investigation into language model capabilities and behavior.
Things to try
One interesting aspect of the OLMoE-1B-7B-0924 model is its Mixture-of-Experts architecture. This allows the model to leverage specialized "experts" for different types of language tasks, potentially improving performance and generalization. Developers could experiment with prompts that target specific capabilities, like math reasoning or common sense inference, to see how the model's different experts respond. Additionally, the open-source nature of the model enables customization and further research into language model architectures and training techniques.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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OLMoE-1B-7B-0924-Instruct
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OLMoE-1B-7B-0924-Instruct is a Mixture-of-Experts language model with 1 billion active and 7 billion total parameters, released in September 2024. It was adapted from the OLMoE-1B-7B model via supervised fine-tuning and direct preference optimization, yielding state-of-the-art performance among models with a similar cost. The model is 100% open-source and can compete with much larger language models like Llama2-13B-Chat. Model inputs and outputs The OLMoE-1B-7B-0924-Instruct model takes in text-based prompts and generates relevant responses. It supports a variety of input formats, including the chat template format used in the example code. Inputs Text-based prompts, ideally structured in a conversational format Outputs Generated text responses to the input prompts Capabilities The OLMoE-1B-7B-0924-Instruct model demonstrates strong performance on a range of benchmarks, including commonsense reasoning, open-ended question answering, and various other language understanding tasks. It is particularly adept at tasks requiring logical reasoning and inference. What can I use it for? The OLMoE-1B-7B-0924-Instruct model can be used for a variety of natural language processing applications, such as building conversational assistants, generating informative content, and aiding in research and development. Its strong performance and open-source availability make it an attractive option for both commercial and academic use cases. Things to try One interesting aspect of the OLMoE-1B-7B-0924-Instruct model is its ability to engage in multi-turn conversations, maintaining context and coherence over longer exchanges. Developers could experiment with using the model in interactive chatbot applications, observing how it responds to follow-up questions and requests for clarification or additional detail.
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MolmoE-1B-0924
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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.
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