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FlexOlmo is a new kind of LM that unlocks a new paradigm of data collaboration. With FlexOlmo, data owners can contribute to the development of open language models without giving up control of their data. There is no need to share raw data directly, and data contributors can decide when their data is active in the model, deactivate it at any time, and receive attributions whenever it's used for inference. > FlexOlmo-7x7B-1T (without router training) is a Mixture-of-Experts with 33B total parameters, combining independently trained experts on public-mix, news, math, code, academic texts, creative writing, and Reddit data. The public-mix expert is trained on 1T tokens of public data while the other experts are branched from the public-mix expert and trained on 50B tokens of their respective data. This information and more can also be found: - **Paper**: https://allenai.org/papers/flexolmo - **Code**: https://github.com/allenai/FlexOlmo - **Blog**: https://allenai.org/blog/flexolmo - **Data and corresponding models**: | Corpus | Public | Math | News | Academic | Code | Creative Writing | Reddit | |------------------|----------------|----------------|----------------|----------------|----------------|------------------|----------------| | Model | Flex-public-7B-1T | Flex-math-2x7B-1T | Flex-news-2x7B-1T | Flex-pes2o-2x7B-1T | Flex-code-2x7B-1T | Flex-creative-2x7B-1T | Flex-reddit-2x7B-1T |

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Tülu 3 is a leading instruction following model family, offering a post-training package with fully open-source data, code, and recipes designed to serve as a comprehensive guide for modern techniques. This is one step of a bigger process to training fully open-source models, like our OLMo models. 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. The Tülu3 models have limited safety training, but are not deployed automatically with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base Llama 3.1 models, however it is likely to have included a mix of Web data and technical sources like books and code. See the Falcon 180B model card for an example of this. PPO settings for RLVR: - **Learning Rate**: 3 × 10⁻⁷ - **Discount Factor (gamma)**: 1.0 - **General Advantage Estimation (lambda)**: 0.95 - **Mini-batches (N_mb)**: 1 - **PPO Update Iterations (K)**: 4 - **PPO's Clipping Coefficient (epsilon)**: 0.2 - **Value Function Coefficient (c1)**: 0.1 - **Gradient Norm Threshold**: 1.0 - **Learning Rate Schedule**: Linear - **Generation Temperature**: 1.0 - **Batch Size (effective)**: 224 - **Max Token Length**: 2,048 - **Max Prompt Token Length**: 2,048 - **Penalty Reward Value for Responses without an EOS Token**: -10.0 - **Response Length**: 2,048 - **Total Episodes**: 100,000 - **KL penalty coefficient (beta)**: 0.05 - **Warm up ratio (omega)**: 0.0

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Tülu3 is a leading instruction following model family, offering fully open-source data, code, and recipes designed to serve as a comprehensive guide for modern post-training techniques. Tülu3 is designed for state-of-the-art performance on a diversity of tasks in addition to chat, such as MATH, GSM8K, and IFEval. The Tülu3 models have limited safety training, but are not deployed automatically with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base Llama 3.1 models, however it is likely to have included a mix of Web data and technical sources like books and code. See the Falcon 180B model card for an example of this. DPO: - **Learning Rate**: 5 × 10⁻⁷ (8B), 2.0e-7 (70B, 405B) - **Learning Rate Schedule**: Linear - **Batch Size (effective)**: 128 (8B), 128 (70B), 256(405B) - **KL Penalty Coefficient**: 5 - **Warm-up Ratio**: 0.1 - **Max Sequence Length**: 2,048 - **Epochs**: 1

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Tülu3 is a leading instruction following model family, offering fully open-source data, code, and recipes designed to serve as a comprehensive guide for modern post-training techniques. Tülu3 is designed for state-of-the-art performance on a diversity of tasks in addition to chat, such as MATH, GSM8K, and IFEval. The Tülu3 models have limited safety training, but are not deployed automatically with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base Llama 3.1 models, however it is likely to have included a mix of Web data and technical sources like books and code. See the Falcon 180B model card for an example of this. SFT: - **Learning Rate**: 5E-6 (8B), 2E-6 (70B, 405B) - **Effective Batch Size:** 128 (8B, 70B), 256 (405B) - **Max. Sequence Length:** 4096 - **Loss Accumulation:** Sum (see https://unsloth.ai/blog/gradient) - **Learning Rate Schedule:** Linear - **LR Warmup Ratio:** 0.03 - **Num. Epochs:** 2

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OLMo 1B July 2024 is the latest version of the original OLMo 1B model rocking a 4.4 point increase in HellaSwag, among other evaluations improvements, from an improved version of the Dolma dataset and staged training. **This version is for direct use with HuggingFace Transformers** from v4.40 on. OLMo is a series of **O**pen **L**anguage **Mo**dels designed to enable the science of language models. The OLMo models are trained on the Dolma dataset. We release all code, checkpoints, logs, and details involved in training these models. The core models released in this batch are the following: | Size | Training Tokens | Layers | Hidden Size | Attention Heads | Context Length | |------|--------|---------|-------------|-----------------|----------------| | OLMo 1B July 2024 | 3.05 Trillion | 16 | 2048 | 16 | 4096 | | OLMo 7B July 2024 | 2.75 Trillion | 32 | 4096 | 32 | 4096 |

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OLMo-1B-hf

allenai

OLMo is a series of **O**pen **L**anguage **Mo**dels designed to enable the science of language models. The OLMo models are trained on the Dolma dataset. We release all code, checkpoints, logs (coming soon), and details involved in training these models. This model has been converted from allenai/OLMo-1B for the Hugging Face Transformers format. The core models released in this batch are the following: | Size | Training Tokens | Layers | Hidden Size | Attention Heads | Context Length | |------|--------|---------|-------------|-----------------|----------------| | OLMo 1B | 3 Trillion |16 | 2048 | 16 | 2048 | | OLMo 7B | 2.5 Trillion | 32 | 4096 | 32 | 2048 | | OLMo 7B Twin 2T | 2 Trillion | 32 | 4096 | 32 | 2048 | We are releasing many checkpoints for these models, for every 1000 training steps. These have not yet been converted into Hugging Face Transformers format, but are available in allenai/OLMo-1B.

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We introduce OLMo 2 32B, the largest model in the OLMo 2 family. OLMo 2 was pre-trained on OLMo-mix-1124 and uses Dolmino-mix-1124 for mid-training. OLMo 2 is the latest in a series of **O**pen **L**anguage **Mo**dels designed to enable the science of language models. We have released all code, checkpoints, logs, and associated training details on GitHub. 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:

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OLMo 2 32B Instruct March 2025 is post-trained variant of the OLMo-2 32B March 2025 model, which has undergone supervised finetuning on an OLMo-specific variant of the Tülu 3 dataset, further DPO training on this dataset, and final RLVR training on this dataset. 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 OLMo 2 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, and associated training details. OLMo 2 will be supported in the next version of Transformers, and you need to install it from the main branch using:

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