OLMo 3 7B Think
Parameters
7B
Context Length
65.536K
Modality
Text
Architecture
Dense
License
Apache 2.0
Release Date
25 Oct 2025
Knowledge Cutoff
Dec 2024
Technical Specifications
Attention Structure
Multi-Head Attention
Hidden Dimension Size
4096
Number of Layers
32
Attention Heads
32
Key-Value Heads
32
Activation Function
SwigLU
Normalization
-
Position Embedding
Absolute Position Embedding
System Requirements
VRAM requirements for different quantization methods and context sizes
OLMo 3 7B Think
The OLMo 3 7B Think model is a specialized variant within the OLMo 3 family, developed by the Allen Institute for AI (Ai2). This model is engineered to address complex problems requiring multi-step logical inference by making its reasoning process transparent. It is designed to surface intermediate thinking steps, providing researchers and developers with explicit thinking tokens to examine the model's internal deliberations before reaching a final answer. This capability supports enhanced interpretability and auditability of AI systems.
Architecturally, OLMo 3 7B Think is a Transformer-style autoregressive language model with a dense architecture, comprising 7 billion parameters. It utilizes a multi-headed attention mechanism and incorporates Rotary Position Embeddings (RoPE) with scaling to support an extended context length of up to 65,536 tokens. The model's training methodology involves a multi-stage approach. It is initially pre-trained on the comprehensive Dolma 3 dataset and subsequently post-trained through Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Reinforcement Learning from Verifiable Rewards (RLVR) on custom Dolci-Think datasets. This layered training focuses on imbuing the model with robust reasoning skills, particularly in domains such as mathematics and coding, while ensuring the model's 'thought process' is explicitly generated.
This variant is optimized for reasoning-intensive tasks, providing a capable foundation for academic research and practical Natural Language Processing (NLP) workflows that demand transparent problem-solving. Its design allows for efficient, inspectable reasoning capabilities, making advanced AI accessible on more modest hardware. The full transparency of the OLMo project, which includes the release of all training data, code, checkpoints, and associated training details under an Apache 2.0 license, fosters reproducibility and further scientific inquiry into model development and behavior.
About OLMo 3
OLMo (Open Language Model) is a series of fully open language models designed to enable the science of language models. Released by the Allen Institute for AI (Ai2), OLMo 3 provides complete access to training data (Dolma 3), code, checkpoints, logs, and evaluation methodologies. The family includes Base models for pretraining research, Instruct variants for chat and tool use, and Think variants with chain-of-thought reasoning capabilities. All models are trained with staged approach including pretraining, mid-training, and long-context phases.
Other OLMo 3 Models
Evaluation Benchmarks
Ranking is for Local LLMs.
No evaluation benchmarks for OLMo 3 7B Think available.
Rankings
Overall Rank
-
Coding Rank
-
GPU Requirements
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Context Size: 1,024 tokens