Trinity-Large-TrueBase is a base pretraining checkpoint from Arcee AI's Trinity Large training run. It is a 398B-parameter sparse Mixture-of-Experts (MoE) model with approximately 13B active parameters per token. The checkpoint was captured after 10 trillion tokens of pretraining, prior to learning-rate annealing and before any instruction tuning or reinforcement learning.
This checkpoint is intended for research, probing, ablation studies, and downstream fine-tuning and comes without any pre-baked alignment, instruction formatting, or preference optimization.
More details on the training of Trinity Large are available in the technical report.
Model Variants
The Trinity Large family consists of three checkpoints from the same training run:
Trinity-Large-TrueBase (this release): 10T-token pre-anneal checkpoint with no instruction data
Trinity-Large-Thinking: Reasoning-optimized, agentic post-training with extended chain-of-thought
Trinity-Large-Base: Full 17T-token pretrained foundation model with mid-training anneals
Studying emergent behavior from large-scale pretraining
Sparse MoE routing and load-balancing research
Interpretability, probing, and ablation studies
Domain-specific fine-tuning from a clean base
Academic and industrial foundation model research
Rationale for Release
Most base model releases include instruction data, annealed training dynamics, or early alignment stages. Trinity-Large-TrueBase excludes these, providing an opportunity to study what large-scale models learn from pretraining data alone. This checkpoint is intended as a foundation for research rather than as a finished conversational assistant.
Known Limitations
Not aligned for safety, helpfulness, or conversational tone
Requires substantial compute and expertise to fine-tune
May exhibit raw or unstable behaviors typical of unaligned models
No extended-context tuning beyond the 8K pretraining window
License
Trinity-Large-TrueBase is released under the OpenMDW License, version 1.1 (OpenMDW-1.1).
