Full BF16 version of olmOCR-2-7B-1025-FP8. We recommend using the FP8 version for all practical purposes except further fine tuning. This is a release of the olmOCR model that's fine tuned from Qwen2.5-VL-7B-Instruct using the olmOCR-mix-1025 dataset. It has been additionally fine tuned using GRPO RL training to boost its performance at math equations, tables, and other tricky OCR cases. Quick links: - 📃 Paper - 🤗 SFT Dataset - 🤗 RL Dataset - 🛠️ Code - 🎮 Demo
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Quantized to FP8 Version of olmOCR-2-7B-1025, using llmcompressor. This is a release of the olmOCR model that's fine tuned from Qwen2.5-VL-7B-Instruct using the olmOCR-mix-1025 dataset. It has been additionally fine tuned using GRPO RL training to boost its performance at math equations, tables, and other tricky OCR cases. Quick links: - 📃 Paper - 🤗 SFT Dataset - 🤗 RL Dataset - 🛠️ Code - 🎮 Demo
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This is a preview release of the olmOCR model that's fine tuned from Qwen2-VL-7B-Instruct using the olmOCR-mix-0225 dataset. Quick links: - 📃 Paper - 🤗 Dataset - 🛠️ Code - 🎮 Demo The best way to use this model is via the olmOCR toolkit. The toolkit comes with an efficient inference setup via sglang that can handle millions of documents at scale.
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Quantized to FP8 Version of olmOCR-7B-0825, using llmcompressor. This is a release of the olmOCR model that's fine tuned from Qwen2.5-VL-7B-Instruct using the olmOCR-mix-0225 dataset. Quick links: - 📃 Paper - 🤗 Dataset - 🛠️ Code - 🎮 Demo
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> OLMoE-1B-7B is a Mixture-of-Experts LLM with 1B active and 7B total parameters released in January 2025 (0125) that is 100% open-source. It is an improved version of OLMoE-09-24, see the paper appendix for details. Important branches: - `step1200000-tokens5033B`: Pretraining checkpoint used for annealing. There are a few more checkpoints after this one but we did not use them. - `main`: Checkpoint annealed from `step1200000-tokens5033B` for an additional 100B tokens (23,842 steps). We use this checkpoint for our adaptation (https://huggingface.co/allenai/OLMoE-1B-7B-0125-SFT & https://huggingface.co/allenai/OLMoE-1B-7B-0125-Instruct). - `fp32`: FP32 version of `main`. The model weights were stored in FP32 during training but we did not observe any performance drop from casting them to BF16 after training so we upload all weights in BF16. If you want the original FP32 checkpoint for `main` you can use this one. You will find that it yields slightly different results but should perform around the same on benchmarks.
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OLMoE-1B-7B-0125-Instruct January 2025 is post-trained variant of the OLMoE-1B-7B January 2025 model, which has undergone supervised finetuning on an OLMo-specific variant of the Tülu 3 dataset and further DPO training on this dataset, and finally RLVR training using this data. Tülu 3 is designed for state-of-the-art performance on a diversity of tasks in addition to chat, such as MATH, GSM8K, and IFEval. Check out the OLMoE paper or Tülu 3 paper for more details! OLMo is a series of **O**pen **L**anguage **Mo**dels designed to enable the science of language models. These models are trained on the Dolma dataset. We are releasing all code, checkpoints, logs (coming soon), and associated training details. The core models released in this batch include the following: - **Model type:** A model trained on a mix of publicly available, synthetic and human-created datasets. - **Language(s) (NLP):** Primarily English - **License:** Apache 2.0 - **Finetuned from model:** allenai/OLMoE-1B-7B-0125-DPO
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> OLMoE-1B-7B is a Mixture-of-Experts LLM with 1B active and 7B total parameters released in September 2024 (0924). It 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. This information and more can also be found on the **OLMoE GitHub repository**. - **Paper**: https://arxiv.org/abs/2409.02060 - **Pretraining** Checkpoints, Code, Data and Logs. - **SFT (Supervised Fine-Tuning)** Checkpoints, Code, Data and Logs. - **DPO/KTO (Direct Preference Optimization/Kahneman-Tversky Optimization)**, Checkpoints, Preference Data, DPO code, KTO code and Logs. Install `transformers` **from source** until a release after this PR & `torch` and run:
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> OLMoE-1B-7B-Instruct is a Mixture-of-Experts LLM with 1B active and 7B total parameters released in September 2024 (0924) that has been adapted via SFT and DPO from OLMoE-1B-7B. It yields state-of-the-art performance among models with a similar cost (1B) and is competitive with much larger models like Llama2-13B-Chat. OLMoE is 100% open-source. This information and more can also be found on the **OLMoE GitHub repository**. - **Paper**: https://arxiv.org/abs/2409.02060 - **Pretraining** Checkpoints, Code, Data and Logs. - **SFT (Supervised Fine-Tuning)** Checkpoints, Code, Data and Logs. - **DPO/KTO (Direct Preference Optimization/Kahneman-Tversky Optimization)**, Checkpoints, Preference Data, DPO code, KTO code and Logs. Branches: - `main`: Preference tuned via DPO model of https://hf.co/allenai/OLMoE-1B-7B-0924-SFT (`main` branch) - `load-balancing`: Ablation with load balancing loss during DPO starting from the `load-balancing` branch of https://hf.co/allenai/OLMoE-1B-7B-0924-SFT - `non-annealed`: Ablation starting from the `non-annealed` branch of https://hf.co/allenai/OLMoE-1B-7B-0924-SFT which is an SFT of the pretraining checkpoint prior to annealing (branch `step1200000-tokens5033B` of https://hf.co/allenai/OLMoE-1B-7B-0924) - `kto`: Ablation using KTO instead of DPO. This branch is the checkpoint after 5,000 steps with the RMS optimizer. The other `kto*` branches correspond to the other checkpoints mentioned in the paper.
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OlmoEarth-v1_1-Base is a ViT-Base (114M parameters) foundation model for remote sensing tasks over Sentinel-1, Sentinel-2, and Landsat images or image time series. This model is presented in the paper OlmoEarth v1.2: A more efficient family of OlmoEarth models. - GitHub: https://github.com/allenai/olmoearth_pretrain - Paper: https://allenai.org/papers/olmoearth_v1_2
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OlmoEarth-v1_2-Nano is a ViT-Nano (1.7M parameters) foundation model for remote sensing tasks over Sentinel-1, Sentinel-2, and Landsat images or image time series. If you are interested in computing embeddings with OlmoEarth, or fine-tuning OlmoEarth for a downstream task, see our tutorials: The OlmoEarth model is deployed in the OlmoEarth Platform, a solution that enables any organization to leverage our models and accelerate their mission, without requiring deep AI or engineering expertise. Sign up to get updates and partner access.
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