We introduce Olmo 3, a new family of 7B and 32B models both Instruct and Think variants. Long chain-of-thought thinking improves reasoning tasks like math and coding. Olmo is a series of Open language models designed to enable the science of language models. These models are pre-trained on the Dolma 3 dataset and post-trained on the Dolci datasets. We are releasing all code, checkpoints, logs (coming soon), and associated training details. The RL-Zero family of models is an experimental set of model for the scientific exploration of RLVR training. You can use OLMo with the standard HuggingFace transformers library: For faster performance, you can quantize the model using the following method: The quantized model is more sensitive to data types and CUDA operations. To avoid potential issues, it’s recommended to pass the inputs directly to CUDA using: Model fine-tuning can be done from the final checkpoint (the main revision of this model) or the base model. You can override most configuration options from the command-line. For example, to override the learning rate you could launch the script like this: — Developed by: Allen Institute for AI (Ai2) — Model type: a Transformer style…
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Автор: allenai
Теги: olmo2-retrofit, conversational, en, endpoints_compatible
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Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
