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arcee-ai/Trinity-Large-TrueBase

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Model Card

<div align="center"> <picture> <img src="https://cdn-uploads.huggingface.co/production/uploads/6435718aaaef013d1aec3b8b/i-v1KyAMOW_mgVGeic9WJ.png" alt="Arcee Trinity Large" style="max-width: 100%; height: auto;"

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Trinity-Large-TrueBase

Introduction

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](https://huggingface.co/arcee-ai/Trinity-Large-Thinking): Reasoning-optimized, agentic post-training with extended chain-of-thought
—[Trinity-Large-Base](https://huggingface.co/arcee-ai/Trinity-Large-Base): Full 17T-token pretrained foundation model with mid-training anneals
—[Trinity-Large-Preview](https://huggingface.co/arcee-ai/Trinity-Large-Preview): Lightly post-trained, chat-ready model undergoing active RL

Architecture

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

Hyperparameter	Value
Total parameters	~398B
Active parameters per token	~13B
Experts	256
Active experts	4
Routing strategy	4-of-256 (1.56% sparsity)
Dense layers	6
Pretraining context length	8,192
Architecture	Sparse MoE (AfmoeForCausalLM)

Note: Extended context support (e.g., 512k) was introduced after this checkpoint and is not available in TrueBase.

Benchmark Results

Benchmark	N-shot	Metric	Score	Stderr
arcchallenge0shot	0	acc_norm,none	0.6237	±0.0142
bbh_fewshot	3	exactmatch,removewhitespace	0.5784	±0.0054
gpqadiamond5shot	5	acc_norm,none	0.4091	±0.0350
gpqadiamondgenerative_5shot	5	exact_match,flexible-extract	0.3788	±0.0346
gsm8k_8shot	8	exact_match,flexible-extract	0.8036	±0.0109
gsm8k_cot	8	exact_match,flexible-extract	0.8044	±0.0109
hellaswag_5shot	5	acc_norm,none	0.8813	±0.0032
humaneval_plus	0	pass@1,create_test	0.5183	±0.0391
leaderboardmathhard	4	exact_match,none	0.2696	±0.0113
mbpp_plus	3	passat1,none	0.8095	±0.0202
minerva_math500	4	math_verify,none	0.4820	±0.0224
mmlu_5shot	5	acc,none	0.7845	±0.0033
mmlugenerative5shot	5	exactmatch,getresponse	0.7848	±0.0033
mmlu_pro	5	exact_match,custom-extract	0.5160	±0.0044
triviaqa_5shot	5	exactmatch,removewhitespace	0.8096	±0.0029
winogrande_5shot	5	acc,none	0.8145	±0.0109

Training Configuration

Pretraining

—Training tokens: 10 trillion
—Checkpoint type: Pre-anneal
—Instruction data: None
—RLHF or post-training: None

This checkpoint branches from the main Trinity Large run at the 10T-token mark, prior to learning-rate decay or post-training phases.

Optimizers

Optimizer learning rates after WSD warm-up:

—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

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 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).