Instructions to use arcee-ai/Trinity-Large-TrueBase with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use arcee-ai/Trinity-Large-TrueBase with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="arcee-ai/Trinity-Large-TrueBase", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("arcee-ai/Trinity-Large-TrueBase", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("arcee-ai/Trinity-Large-TrueBase", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use arcee-ai/Trinity-Large-TrueBase with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "arcee-ai/Trinity-Large-TrueBase"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "arcee-ai/Trinity-Large-TrueBase",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/arcee-ai/Trinity-Large-TrueBase

SGLang

How to use arcee-ai/Trinity-Large-TrueBase with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "arcee-ai/Trinity-Large-TrueBase" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "arcee-ai/Trinity-Large-TrueBase",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "arcee-ai/Trinity-Large-TrueBase" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "arcee-ai/Trinity-Large-TrueBase",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use arcee-ai/Trinity-Large-TrueBase with Docker Model Runner:
```
docker model run hf.co/arcee-ai/Trinity-Large-TrueBase
```

Trinity-Large-TrueBase / README.md

annekethvij

Adopt OpenMDW-1.1 license

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	---
	license: other
	language:
	- en
	- es
	- fr
	- de
	- it
	- pt
	- ru
	- ar
	- hi
	- ko
	- zh
	library_name: transformers
	license_link: LICENSE
	license_name: openmdw-1.1
	---
	<!-- markdownlint-disable first-line-h1 -->
	<!-- markdownlint-disable html -->
	<!-- markdownlint-disable no-duplicate-header -->

	<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;"
	>
	</picture>
	</div>
	<hr>


	# 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](https://github.com/arcee-ai/trinity-large-tech-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 \|
	\|-------------------------------\|--------\|-------------------------------\|--------\|---------\|
	\| arc_challenge_0shot \| 0 \| acc_norm,none \| 0.6237 \| ±0.0142 \|
	\| bbh_fewshot \| 3 \| exact_match,remove_whitespace \| 0.5784 \| ±0.0054 \|
	\| gpqa_diamond_5shot \| 5 \| acc_norm,none \| 0.4091 \| ±0.0350 \|
	\| gpqa_diamond_generative_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 \|
	\| leaderboard_math_hard \| 4 \| exact_match,none \| 0.2696 \| ±0.0113 \|
	\| mbpp_plus \| 3 \| pass_at_1,none \| 0.8095 \| ±0.0202 \|
	\| minerva_math500 \| 4 \| math_verify,none \| 0.4820 \| ±0.0224 \|
	\| mmlu_5shot \| 5 \| acc,none \| 0.7845 \| ±0.0033 \|
	\| mmlu_generative_5shot \| 5 \| exact_match,get_response \| 0.7848 \| ±0.0033 \|
	\| mmlu_pro \| 5 \| exact_match,custom-extract \| 0.5160 \| ±0.0044 \|
	\| triviaqa_5shot \| 5 \| exact_match,remove_whitespace \| 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](https://www.primeintellect.ai/)
	- Data partner: [Datology](https://www.datologyai.com/)

	<div align="center">
	<picture>
	<img src="https://cdn-uploads.huggingface.co/production/uploads/6435718aaaef013d1aec3b8b/sSVjGNHfrJKmQ6w8I18ek.png" style="background-color:ghostwhite;padding:5px;" width="17%" alt="Powered by Datology">
	</picture>
	</div>

	<div align="center">
	<picture>
	<img src="https://cdn-avatars.huggingface.co/v1/production/uploads/61e020e4a343274bb132e138/H2mcdPRWtl4iKLd-OYYBc.jpeg" style="background-color:ghostwhite;padding:5px;" width="17%" alt="Powered by Datology">
	</picture>
	</div>


	## 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).