OLMoE-1B-7B-0924
Maintainer: allenai
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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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