The model's architecture is extensively documented in the official technical report and Hugging Face model card. It is a dense, decoder-only transformer with 64 layers and a hidden dimension of 5120. Specific details provided include the use of Grouped-Query Attention (GQA) with 40 attention heads and 8 KV heads, RMSNorm, and SwiGLU activations. The long-context mechanism is thoroughly described as a hybrid approach using sliding-window attention (4K window) in most layers and full-sequence attention in every fourth layer, combined with YaRN-style scaling for Rotary Position Embeddings (RoPE) to reach 65,536 tokens. The training methodology is disclosed as a three-stage process: initial pretraining, mid-training, and context extension.

The Allen Institute for AI provides exemplary transparency regarding the training data. The model was trained on the Dolma 3 dataset, which is publicly available. The composition is broken down into specific stages: Stage 1 used the 5.5T token 'dolma3_mix-1125'; Stage 2 (Mid-training) used the 100B token 'dolma3-dolmino-mix' (math, code, science, and reasoning traces); and Stage 3 (Long Context) used the 100B token 'dolma3-longmino-mix' (34% long-context PDFs, 66% mid-training data). The data collection methodology, including the use of olmOCR for scientific PDFs and gzip-based quality filtering, is fully documented. De-contamination procedures against benchmark test sets are explicitly stated.

The tokenizer is publicly accessible via the Hugging Face repository and is integrated with the standard 'transformers' library. While the exact vocabulary size and training data alignment are implied by its release alongside the model and its use in the Dolma 3 pipeline, the technical report confirms it is a BPE-based tokenizer designed for the specific data mix. The tokenizer's behavior is verifiable through the provided inference code snippets.

The parameter count is clearly stated as 32 billion. As a dense model, all parameters are active during inference, and the provider explicitly confirms there is no 'MoE trickery.' A complete architectural breakdown is provided, including the number of layers (64), hidden size (5120), and attention head configuration (40 Q, 8 KV), allowing for precise verification of the parameter density.

The provider discloses significant details about the training hardware and duration. For example, the 32B Think variant's RL extension was trained for 21 days on 224 GPUs. The technical report mentions the use of the 'Augusta' cluster and specific environment variables (NCCL) required for the run. However, a consolidated total GPU-hour figure for the entire three-stage pretraining of the base model and a specific carbon footprint calculation are less prominently featured than the architectural and data details.

The Allen Institute provides high transparency for evaluations through the 'OLMo-Eval' GitHub repository, which contains the code and configurations used for benchmarking. The technical report specifies the benchmarks used (e.g., MMLU, GSM8K, RULER, HELMET) and provides detailed results across various checkpoints. The release of intermediate checkpoints (stage1-stepXXX, etc.) further enables researchers to reproduce and verify the model's learning trajectory.

The model and its variants (Base, Instruct, Think, RL Zero) are clearly labeled with semantic versioning (e.g., OLMo 3, OLMo 3.1). The model card provides a clear identity and intended use cases. There are no reported issues of the model misidentifying itself as a competitor's product, and the 'Think' variant's reasoning traces provide additional transparency into its internal processing identity.

The model, weights, and code are all released under the highly permissive Apache 2.0 license. There are no conflicting terms or 'open-ish' restrictions on commercial use. The license is clearly stated on the Hugging Face model card, the GitHub repository, and the official blog posts.

VRAM requirements are well-documented by both the provider and the community. Official documentation notes that FP16 inference requires approximately 64GB of VRAM, making it suitable for single A100 (80GB) or multi-GPU consumer setups (e.g., 4x RTX 4090). Quantization support (INT8/INT4) is documented, with community guides providing specific VRAM targets (sub-20GB for 4-bit). The impact of context length on memory (KV cache efficiency via GQA) is also detailed.

The project maintains a detailed CHANGELOG.md in the 'OLMo-core' repository, tracking updates to the training code, configs, and model releases. The transition from OLMo 3 to OLMo 3.1 is documented with specific performance gains and training changes (e.g., additional RL training days). The availability of intermediate checkpoints allows users to pin specific versions to avoid silent drift.