Trinity-Large-Base

Image: Arcee Trinity Large

Trinity-Large-Base#

Introduction#

Trinity-Large-Base is a pretrained foundation model 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 17 trillion tokens of pretraining, including mid-training learning-rate anneals and context extension, but prior to any instruction tuning or reinforcement learning.

This checkpoint represents the completed pretraining phase and serves as a foundation for research and downstream fine-tuning.

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-Base (this release): Full 17T-token pretrained foundation model with mid-training anneals
• Trinity-Large-Thinking: Reasoning-optimized, agentic post-training with extended chain-of-thought
• Trinity-Large-TrueBase: 10T-token pre-anneal checkpoint with no instruction data
• Trinity-Large-Preview: Lightly post-trained, chat-ready model undergoing active RL

Architecture#

Trinity-Large-Base uses a sparse MoE configuration designed to maximize efficiency while maintaining large-scale capacity.

Benchmark Results#

Training Configuration#

Pretraining#

• Training tokens: 17 trillion
• Checkpoint type: Post-anneal (foundation)
• Instruction data: None
• RLHF or post-training: None

This checkpoint represents the final pretrained state after completion of the pretraining phase, including mid-training learning-rate anneals, but before instruction tuning or reinforcement learning.

Optimizers#

Optimizer learning rates during WSD stable phase:

• Adam learning rate: 2e-4
• Muon learning rate: 8e-4

Muon was used to support larger critical batch sizes in a highly sparse MoE regime.

Infrastructure#

• Hardware: 2,048 NVIDIA B300 GPUs
• Parallelism: HSDP + Expert Parallelism
• Compute partner: Prime Intellect
• Data partner: Datology

Image: Powered by Datology

Image: Powered by Datology

Intended Use#

• 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 pretrained foundation
• Academic and industrial foundation model research

Comparison with TrueBase#

Trinity-Large-Base includes an additional 7 trillion training tokens compared to Trinity-Large-TrueBase, along with mid-training learning-rate anneals. These anneals stabilize training dynamics and typically improve downstream fine-tuning performance compared to the pre-anneal checkpoint. Researchers studying raw pretraining dynamics may prefer TrueBase, while those seeking a foundation for fine-tuning may prefer this checkpoint.

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-Base is released under the OpenMDW License, version 1.1 (OpenMDW-1.1).