--- license: apache-2.0 language: - en library_name: transformers datasets: - allenai/dolma3_mix-5.5T-1125 model-index: - name: Olmo-3-1125-32B results: - task: type: text-generation dataset: name: Benchmarks type: benchmark metrics: - name: Olmo 3-Eval Math type: olmo_3_eval_math value: 61.6 - name: BigCodeBench type: bigcodebench value: 43.9 - name: HumanEval type: humaneval value: 66.5 - name: DeepSeek LeetCode type: deepseek_leetcode value: 1.9 - name: DS 1000 type: ds_1000 value: 29.7 - name: MBPP type: mbpp value: 60.2 - name: MultiPL HumanEval type: multipl_humaneval value: 35.9 - name: MultiPL MBPPP type: multipl_mbppp value: 41.8 - name: Olmo 3-Eval Code type: olmo_3_eval_code value: 40.0 - name: ARC MC type: arc_mc value: 94.7 - name: MMLU STEM type: mmlu_stem value: 70.8 - name: MedMCQA MC type: medmcqa_mc value: 57.6 - name: MedQA MC type: medqa_mc value: 53.8 - name: SciQ MC type: sciq_mc value: 95.5 - name: Olmo 3-Eval MC_STEM type: olmo_3_eval_mc_stem value: 74.5 - name: MMLU Humanities type: mmlu_humanities value: 78.3 - name: MMLU Social Sci. type: mmlu_social_sci. value: 83.9 - name: MMLU Other type: mmlu_other value: 75.1 - name: CSQA MC type: csqa_mc value: 82.3 - name: PIQA MC type: piqa_mc value: 85.6 - name: SocialIQA MC type: socialiqa_mc value: 83.9 - name: CoQA Gen2MC MC type: coqa_gen2mc_mc value: 96.4 - name: DROP Gen2MC MC type: drop_gen2mc_mc value: 87.2 - name: Jeopardy Gen2MC MC type: jeopardy_gen2mc_mc value: 92.3 - name: NaturalQs Gen2MC MC type: naturalqs_gen2mc_mc value: 78.0 - name: SQuAD Gen2MC MC type: squad_gen2mc_mc value: 98.2 - name: Olmo 3-Eval MC_Non-STEM type: olmo_3_eval_mc_non_stem value: 85.6 - name: HellaSwag RC type: hellaswag_rc value: 84.8 - name: Winogrande RC type: winogrande_rc value: 90.3 - name: Lambada type: lambada value: 75.7 - name: Basic Skills type: basic_skills value: 93.5 - name: DROP type: drop value: 81.0 - name: Jeopardy type: jeopardy value: 75.3 - name: NaturalQs type: naturalqs value: 48.7 - name: SQuAD type: squad value: 94.5 - name: CoQA type: coqa value: 74.1 - name: Olmo 3-Eval GenQA type: olmo_3_eval_genqa value: 79.8 - name: BBH type: bbh value: 77.6 - name: MMLU Pro MC type: mmlu_pro_mc value: 49.6 - name: Deepmind Math type: deepmind_math value: 30.1 - name: LBPP type: lbpp value: 21.7 source: name: Model README url: https://huggingface.co/allenai/Olmo-3-1125-32B --- ## Model Details Logo for Olmo 3 32B Base model # Model Card for Olmo 3 32B We introduce Olmo 3, a new family of 7B and 32B models. This suite includes Base, Instruct, and Think variants. The Base models were trained using a staged training approach. 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 3 dataset. We are releasing all code, checkpoints, and associated training details. | Size | Training Tokens | Layers | Hidden Size | Q Heads | KV Heads | Context Length | |--------|-----------------|--------|-------------|---------|----------|----------------| | [OLMo 3 7B](https://huggingface.co/allenai/Olmo-3-1025-7B) | 5.93 Trillion | 32 | 4096 | 32 | 32 | 65,536 | | [OLMo 3 32B](https://huggingface.co/allenai/Olmo-3-1125-32B) | 5.50 Trillion | 64 | 5120 | 40 | 8 | 65,536 | The core models released in this batch include the following: | **Stage** | **Olmo 3 7B Think** | **Olmo 3 32B Think** | **Olmo 3 7B Instruct** | |--------------------------|-----------------------|------------------------|---------------------------| | **Base Model** | [Olmo-3-7B](https://huggingface.co/allenai/Olmo-3-1025-7B) | [Olmo-3-32B](https://huggingface.co/allenai/Olmo-3-1125-32B) | [Olmo-3-7B](https://huggingface.co/allenai/Olmo-3-1025-7B) | | **SFT** | [Olmo-3-7B-Think-SFT](https://huggingface.co/allenai/Olmo-3-7B-Think-SFT) | [Olmo-3-32B-Think-SFT](https://huggingface.co/allenai/Olmo-3-32B-Think-SFT) | [Olmo-3-7B-Instruct-SFT](https://huggingface.co/allenai/Olmo-3-7B-Instruct-SFT) | | **DPO** | [Olmo-3-7B-Think-DPO](https://huggingface.co/allenai/Olmo-3-7B-Think-DPO) | [Olmo-3-32B-Think-DPO](https://huggingface.co/allenai/Olmo-3-32B-Think-DPO) | [Olmo-3-7B-Instruct-DPO](https://huggingface.co/allenai/Olmo-3-7B-Instruct-DPO) | | **Final Models (RLVR)** | [Olmo-3-7B-Think](https://huggingface.co/allenai/Olmo-3-7B-Think) | [Olmo-3-32B-Think](https://huggingface.co/allenai/Olmo-3-32B-Think) | [Olmo-3-7B-Instruct](https://huggingface.co/allenai/Olmo-3-7B-Instruct) | ## Installation Olmo 3 is supported in transformers v4.57.0 or higher: ```bash pip install transformers>=4.57.0 ``` ## Inference You can use OLMo with the standard HuggingFace transformers library: ```python from transformers import AutoModelForCausalLM, AutoTokenizer olmo = AutoModelForCausalLM.from_pretrained("allenai/Olmo-3-1125-32B") tokenizer = AutoTokenizer.from_pretrained("allenai/Olmo-3-1125-32B") message = ["Language modeling is "] inputs = tokenizer(message, return_tensors='pt', return_token_type_ids=False) # optional verifying cuda # inputs = {k: v.to('cuda') for k,v in inputs.items()} # olmo = olmo.to('cuda') response = olmo.generate(**inputs, max_new_tokens=100, do_sample=True, top_k=0, temperature=1.0, top_p=0.7) print(tokenizer.batch_decode(response, skip_special_tokens=True)[0]) >> 'Language modeling is a key component of any text-based application, but its effectiveness...' ``` For faster performance, you can quantize the model using the following method: ```python AutoModelForCausalLM.from_pretrained("allenai/Olmo-3-1125-32B", torch_dtype=torch.float16, load_in_8bit=True) # Requires bitsandbytes ``` 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: ```python inputs.input_ids.to('cuda') ``` We have released checkpoints for these models. For pretraining, the naming convention is `stage1-stepXXX`. The conventions for midtraining and long context are `stage2-ingredientY-stepXXX` and `stage3-stepXXX`, respectively. To load a specific model revision with HuggingFace, simply add the argument `revision`: ```bash olmo = AutoModelForCausalLM.from_pretrained("allenai/Olmo-3-1125-32B", revision="stage1-step10000") ``` Or, you can access all the revisions for the models via the following code snippet: ```python from huggingface_hub import list_repo_refs out = list_repo_refs("allenai/Olmo-3-1125-32B") branches = [b.name for b in out.branches] ``` ### Fine-tuning Model fine-tuning can be done from the final checkpoint (the `main` revision of this model) or many intermediate checkpoints. Two recipes for tuning are available. 1. Fine-tune with the OLMo-core repository: ```bash torchrun --nproc-per-node=8 ./src/scripts/official/OLMo3/OLMo-3-1025-32B-pretrain.py run01 ``` You can override most configuration options from the command-line. For example, to override the learning rate you could launch the script like this: ```bash torchrun --nproc-per-node=8 ./src/scripts/official/OLMo3/OLMo-3-1025-32B-pretrain.py run01 --train_module.optim.lr=6e-4 ``` For more documentation, see the [GitHub readme](https://github.com/allenai/OLMo-core). ### Model Description - **Developed by:** Allen Institute for AI (Ai2) - **Model type:** a Transformer style autoregressive language model. - **Language(s) (NLP):** English - **License:** The code and model are released under Apache 2.0. - **Contact:** Technical inquiries: `olmo@allenai.org`. Press: `press@allenai.org` - **Date cutoff:** Dec 2024 ### Model Sources - **Project Page:** https://allenai.org/olmo - **Repositories:** - Core repo (training, inference, fine-tuning etc.): https://github.com/allenai/OLMo-core - Evaluation code: https://github.com/allenai/OLMo-Eval - Further fine-tuning code: https://github.com/allenai/open-instruct - **W&B Report:** https://wandb.ai/ai2-llm/Olmo-3-1125-32B/reports/Olmo-3-32B-November-2025--VmlldzoxNTA4NzAxMw - **Paper:** https://allenai.org/papers/olmo3 ## Evaluation Core model results for MODELS are found below. | Model | Olmo 3-Eval Math | BigCodeBench | HumanEval | DeepSeek LeetCode | DS 1000 | MBPP | MultiPL HumanEval | MultiPL MBPPP | Olmo 3-Eval Code | ARC MC | MMLU STEM | MedMCQA MC | MedQA MC | SciQ MC | Olmo 3-Eval MC_STEM | MMLU Humanities | MMLU Social Sci. | MMLU Other | CSQA MC | PIQA MC | SocialIQA MC | CoQA Gen2MC MC | DROP Gen2MC MC | Jeopardy Gen2MC MC | NaturalQs Gen2MC MC | SQuAD Gen2MC MC | Olmo 3-Eval MC_Non-STEM | HellaSwag RC | Winogrande RC | Lambada | Basic Skills | DROP | Jeopardy | NaturalQs | SQuAD | CoQA | Olmo 3-Eval GenQA | BBH | MMLU Pro MC | Deepmind Math | LBPP | |---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| | **Open-weight Models** | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Qwen-2.5-32B | 64.7 | 48.1 | 65.6 | 8.0 | 43.3 | 69.8 | 49.7 | 53.6 | 48.3 | 97.0 | 79.7 | 68.8 | 68.4 | 97.1 | 82.2 | 85.0 | 88.4 | 81.2 | 89.9 | 93.3 | 86.6 | 96.8 | 86.6 | 97.0 | 79.9 | 97.9 | 89.3 | 86.3 | 87.5 | 76.2 | 94.2 | 53.7 | 74.0 | 39.3 | 64.9 | 40.4 | 68.5 | 81.1 | 61.1 | 40.7 | 40.3 | | Gemma-3-27B | 63.2 | 44.0 | 62.1 | 5.8 | 34.3 | 60.0 | 37.7 | 47.2 | 41.6 | 95.8 | 74.9 | 64.7 | 68.7 | 96.8 | 80.2 | 80.5 | 86.2 | 80.2 | 79.0 | 90.3 | 81.2 | 95.8 | 84.6 | 95.9 | 82.0 | 97.7 | 86.7 | 86.0 | 91.3 | 77.5 | 94.9 | 75.9 | 82.1 | 49.2 | 92.4 | 12.4 | 73.5 | 77.4 | 53.1 | 30.4 | 17.7 | | Mistral-3.1-24B | 59.5 | 46.4 | 65.5 | 0.1 | 36.3 | 61.9 | 39.0 | 47.7 | 42.4 | 96.2 | 70.1 | 68.8 | 70.4 | 96.3 | 81.5 | 82.7 | 88.6 | 81.9 | 80.5 | 91.0 | 81.0 | 94.9 | 86.5 | 97.2 | 84.6 | 97.9 | 87.9 | 86.2 | 90.8 | 79.3 | 91.9 | 74.9 | 80.3 | 45.1 | 92.6 | 61.1 | 78.0 | 81.4 | 58.9 | 35.3 | 30.3 | | Seed-36B | 15.3 | 50.7 | 71.3 | 13.0 | 44.0 | 72.0 | 69.2 | 63.8 | 54.9 | 97.3 | 82.8 | 69.6 | 70.1 | 97.1 | 83.4 | 85.7 | 90.1 | 82.4 | 81.1 | 92.5 | 84.9 | 96.9 | 90.1 | 96.2 | 81.4 | 98.1 | 89.0 | 84.8 | 89.3 | 76.1 | 96.0 | 76.1 | 77.4 | 30.7 | 89.1 | 64.4 | 76.0 | 85.0 | 62.2 | 31.3 | 42.6 | | Gemma-2-27B | 57.5 | 43.4 | 57.5 | 4.7 | 29.7 | 61.7 | 40.3 | 49.7 | 41.0 | 94.1 | 65.8 | 61.8 | 61.0 | 95.1 | 75.6 | 79.3 | 85.8 | 76.9 | 78.1 | 89.0 | 81.0 | 94.3 | 66.6 | 92.0 | 74.5 | 97.5 | 83.2 | 86.7 | 90.8 | 76.9 | 93.2 | 73.2 | 80.7 | 47.1 | 93.0 | 14.9 | 72.9 | 74.8 | 47.6 | 27.6 | 19.7 | | Llama-3.1-70B | 62.0 | 43.4 | 57.4 | 0.2 | 29.5 | 55.5 | 32.2 | 35.9 | 36.3 | 95.2 | 70.0 | 67.8 | 72.3 | 95.4 | 80.1 | 83.4 | 87.4 | 79.4 | 79.0 | 91.5 | 83.5 | 95.1 | 70.3 | 97.1 | 82.4 | 97.7 | 86.1 | 88.4 | 91.7 | 79.6 | 92.4 | 78.3 | 84.0 | 53.1 | 92.9 | 73.9 | 81.6 | 80.8 | 50.4 | 40.3 | 11.8 | | **Fully-open Models** | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Marin-32B | 49.3 | 34.5 | 52.3 | 1.3 | 26.3 | 52.1 | 18.5 | 30.5 | 30.8 | 93.4 | 68.4 | 61.8 | 60.8 | 95.1 | 75.9 | 78.9 | 83.7 | 75.4 | 80.1 | 90.5 | 82.4 | 93.9 | 71.0 | 95.3 | 81.0 | 97.6 | 84.5 | 87.2 | 90.5 | 76.7 | 91.1 | 76.5 | 80.5 | 55.1 | 94.4 | 70.7 | 80.3 | 70.1 | 48.1 | 26.7 | 17.3 | | Apertus-70B | 39.7 | 24.0 | 32.5 | 1.2 | 17.8 | 37.6 | 18.4 | 31.3 | 23.3 | 90.7 | 57.8 | 55.9 | 52.4 | 93.3 | 70.0 | 74.1 | 79.2 | 70.1 | 76.9 | 79.0 | 79.3 | 87.5 | 56.5 | 93.2 | 71.9 | 95.7 | 78.5 | 84.5 | 87.7 | 74.8 | 87.5 | 56.3 | 77.2 | 43.1 | 90.7 | 72.8 | 75.0 | 58.8 | 39.6 | 20.1 | 8.1 | | OLMo 2-32B | 53.9 | 22.2 | 29.4 | 0.8 | 20.4 | 37.1 | 10.5 | 23.2 | 20.5 | 94.4 | 64.7 | 60.2 | 62.2 | 95.1 | 75.3 | 79.7 | 84.5 | 75.6 | 81.2 | 87.7 | 82.3 | 94.4 | 68.6 | 96.6 | 78.6 | 97.4 | 84.2 | 87.5 | 89.4 | 77.0 | 88.7 | 76.3 | 79.1 | 51.4 | 94.0 | 68.7 | 79.1 | 64.6 | 46.9 | 22.0 | 8.2 | | **Olmo 3-32B** | 61.6 | 43.9 | 66.5 | 1.9 | 29.7 | 60.2 | 35.9 | 41.8 | 40.0 | 94.7 | 70.8 | 57.6 | 53.8 | 95.5 | 74.5 | 78.3 | 83.9 | 75.1 | 82.3 | 85.6 | 83.9 | 96.4 | 87.2 | 92.3 | 78.0 | 98.2 | 85.6 | 84.8 | 90.3 | 75.7 | 93.5 | 81.0 | 75.3 | 48.7 | 94.5 | 74.1 | 79.8 | 77.6 | 49.6 | 30.1 | 21.7 | ## Model Details #### Stage 1: Initial Pretraining - Dataset: [dolma3_mix-5.5T-1125](https://huggingface.co/datasets/allenai/dolma3_mix-5.5T-1125) - 5.50T tokens - Coverage: 94.83%+ of total pretraining budget #### Stage 2: Mid-training - Ingredient 1 - Dataset: [dolma3-dolmino-mix-1125](https://huggingface.co/datasets/allenai/dolma3_dolmino_mix-100B-1125) - 100B tokens - Mix composition: web pages, code, math/QA/thinking/instruction/PDFs - Ingredient 2 - Dataset: [dolma3-dolmino-mix-1125](https://huggingface.co/datasets/allenai/dolma3_dolmino_mix-100B-1125) - 100B tokens - Mix composition: web pages, code, math/QA/thinking/instruction/PDFs #### Stage 3: Long Context - Dataset: [dolma3-longmino-mix-1125](https://huggingface.co/datasets/allenai/dolma3_longmino_mix-100B-1125) - 100B tokens - Mix composition: midtraining data and PDFs #### Model Merging - 7B Model: No merging - 32B Model: 2 versions on 100B mix, merged before starting long context run. Final checkpoint is merged 4 final checkpoints. ## Bias, Risks, and Limitations Like any base language model or fine-tuned model without safety filtering, these models can easily be prompted by users to generate harmful and sensitive content. Such content may also be produced unintentionally, especially in cases involving bias, so we recommend that users consider the risks when applying this technology. Additionally, many statements from OLMo or any LLM are often inaccurate, so facts should be verified. ## License This model is licensed under Apache 2.0. It is intended for research and educational use in accordance with [Ai2's Responsible Use Guidelines](https://allenai.org/responsible-use). ## Citation A technical manuscript is forthcoming! Find the paper at: https://allenai.org/papers/olmo3 ## Model Card Contact For errors in this model card, contact `olmo@allenai.org`.