We introduce Trillion-7B-preview, a preview of our latest large language model designed to push the boundaries of multilingual scalability and performance. This model is presented in the paper: Trillion-7B-preview.

When comparing performance to training FLOPs for Trillion-7B-preview with competitive models, our model pushes the Pareto frontier, achieving around 66.5% average performance while using significantly fewer compute (~9.3×10²² FLOPs). It outperforms models like Mistral-7B-Instruct-v0.3 and SOLAR-10.7B-Instruct-v1.0 while remaining competitive with models requiring 3-8× more compute such as Qwen2.5-7B-Instruct and EXAONE-3.5-7.8B-Instruct. For full benchmark results, see tables below.

Average Performance vs. Approximate Training FLOPs

Type: Causal Language Model
Training Stage: Pre-training & Post-training
Architecture: Transformer Decoder with RoPE, SwiGLU, RMSNorm
Number of Parameters: 7.76B
Number of Layers: 32
Number of Attention Heads: 32
Context Length: 4,096
Number of Tokens seen: 2T
Vocab Size: 128,128

Quickstart

Here is a code snippet with apply_chat_template that demonstrates how to load the tokenizer and model and generate text.

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "trillionlabs/Trillion-7B-preview"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Tell me a hilarious knock knock joke."
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    model_inputs["input_ids"],
    attention_mask=model_inputs["attention_mask"],
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

"""
Sure! Here's a classic knock-knock joke that's guaranteed to make you chuckle:
Knock, knock.
Who's there?
Lettuce.
Lettuce who?
Lettuce in, it's too cold out here!
"""

We also support vLLM integration.

vllm serve trillionlabs/Trillion-7B-preview --max-model-len 4096

Evaluation

We select a wide variety of benchmarks that evaluate general reasoning, knowledge recall, coding abilities, mathematical reasoning, and instruction following capabilities. We evaluated Trillion-7B-preview along with several leading large language models of similar size. Our model especially demonstrates strong performance on Korean benchmarks.

Full evaluation settings

Benchmark	Language	Evaluation Setting	Metric
General Reasoning and Reading Comprehension
• HellaSwag	English	0-shot	accuracy
• TruthfulQA_mc1	English	6-shot	accuracy
• TruthfulQA_mc2	English	6-shot	accuracy
• ARC:C	English	0-shot	accuracy
• HAERAE	Korean	3-shot	accuracy
• KoBEST	Korean	5-shot	accuracy
• BBH	English	0-shot, CoT	accuracy
• xwinograd_en	English	0-shot	accuracy
• xwinograd_jp	Japanese	0-shot	accuracy
• xwinograd_zh	Chinese	0-shot	accuracy
Knowledge Recall
• KMMLU	Korean	5-shot	accuracy
• MMLU	English	5-shot	accuracy
• Global-MMLU-Lite-en	English	5-shot	accuracy
• Global-MMLU-Lite-ko	Korean	5-shot	accuracy
• Global-MMLU-Lite-ja	Japanese	5-shot	accuracy
• Global-MMLU-Lite-zh	Chinese	5-shot	accuracy
Coding
• HumanEval	English	0-shot, CoT	pass@1
• MBPP	English	0-shot, CoT	pass@1
Mathematical Reasoning
• GSM8k	English	0-shot, CoT	exact-match
• MATH	English	0-shot, CoT	exact-match
• GPQA	English	4-shot	accuracy
• HRM8k	Korean	0-shot, CoT	exact-match
Instruction Following and Chat
• IFEval	English	0-shot	strict-average
• koIFEval*	Korean	0-shot	strict-average
• MT-Bench**	English	LLM-as-a-judge (gpt-4o-2024-08-06)	LLM score
• KO-MT-Bench**	Korean	LLM-as-a-judge (gpt-4o-2024-08-06)	LLM score
• LogicKor**	Korean	LLM-as-a-judge (gpt-4o-2024-08-06)	LLM score

*Note that koIFEval is our in-house evaluation benchmark for assessing instruction-following capabilities in Korean.
**Note that MT-Bench, KO-MT-Bench, and LogicKor use a 10-point scale.

Benchmark Results

General Reasoning and Factuality

Benchmark	Trillion-7B-preview	EXAONE-3.5-7.8B-Instruct	gemma-2-9b-it	Llama-3.1-8B-Instruct	Qwen2.5-7B-Instruct	SOLAR-10.7B-Instruct-v1.0	Mistral-7B-Instruct-v0.3
HellaSwag	58.94	60.04	59.72	59.81	61.97	68.72	65.79
TruthfulQA_mc1	36.10	40.64	42.96	38.07	47.74	56.18	42.47
TruthfulQA_mc2	54.10	59.74	60.09	54.54	64.72	70.64	59.41
ARC:C	54.44	56.40	62.97	53.58	52.99	60.07	58.11
HAERAE	80.02	76.08	68.01	63.15	65.17	60.86	47.75
KoBEST	79.61	78.57	79.98	70.09	79.24	75.20	66.50
KMMLU	48.09	45.39	46.66	41.41	50.15	41.66	33.59
MMLU	63.52	65.65	72.24	68.32	74.23	65.20	61.84
Global-MMLU-Lite-en	67.75	69.50	76.25	67.50	77.25	71.75	65.50
Global-MMLU-Lite-ko	60.75	60.00	64.25	54.00	59.25	53.75	43.00
Global-MMLU-Lite-ja	60.75	45.75	66.50	54.50	65.75	50.75	50.00
Global-MMLU-Lite-zh	59.50	50.00	63.75	60.25	68.75	57.00	47.25
BBH	41.94	53.30	28.77	43.16	53.68	52.91	45.09
xwinograd_en	87.78	87.10	89.55	88.09	85.63	87.35	88.39
xwinograd_jp	79.98	74.45	80.92	76.02	72.89	72.58	70.70
xwinograd_zh	73.81	69.44	68.06	76.19	81.55	74.60	71.83

Coding

Benchmark	Trillion-7B-preview	EXAONE-3.5-7.8B-Instruct	gemma-2-9b-it	Llama-3.1-8B-Instruct	Qwen2.5-7B-Instruct	SOLAR-10.7B-Instruct-v1.0	Mistral-7B-Instruct-v0.3
HumanEval	55.48	79.26	60.98	67.68	81.71	34.76	36.59
MBPP	40.40	61.40	8.40	39.20	51.00	29.40	36.00

Mathematical Reasoning

Benchmark	Trillion-7B-preview	EXAONE-3.5-7.8B-Instruct	gemma-2-9b-it	Llama-3.1-8B-Instruct	Qwen2.5-7B-Instruct	SOLAR-10.7B-Instruct-v1.0	Mistral-7B-Instruct-v0.3
GSM8k	72.25	87.79	73.69	74.98	88.86	62.93	35.94
MATH	32.70	70.68	-	38.30	71.50	14.38	12.12
GPQA	32.81	38.61	36.83	30.58	34.15	28.35	32.59
HRM8k	30.10	38.99	16.04	-	41.51	20.68	7.89

Instruction Following and Chat

Benchmark	Trillion-7B-preview	EXAONE-3.5-7.8B-Instruct	gemma-2-9b-it	Llama-3.1-8B-Instruct	Qwen2.5-7B-Instruct	SOLAR-10.7B-Instruct-v1.0	Mistral-7B-Instruct-v0.3
IFEval	79.13	81.42	75.48	74.93	75.85	51.61	52.64
koIFEval	66.58	54.65	43.30	36.07	48.55	26.12	34.22
MT-Bench	7.00	8.15	7.81	6.32	7.86	6.76	6.84
KO-MT-Bench	6.27	8.13	7.01	4.27	6.31	2.89	4.07
LogicKor	8.14	9.25	8.33	6.45	7.99	1.85	4.76

Limitations

Language Support: The model is optimized for English, Korean, Japanese, and Chinese. Usage with other languages may result in degraded performance.
Knowledge Cutoff: The model's information is limited to data available up to August 2023.
Safety Mechanisms: This release does not yet include comprehensive safety features. Future updates will address this area.
Release Status: This is a preliminary release version with planned enhancements and updates forthcoming.

License

This model repository is licensed under the Apache-2.0 License.

Citation

@article{trillion7Bpreview,
  title={Trillion-7B-preview},
  author={trillionlabs},
  year={2025},
  url={https://huggingface.co/trillionlabs/Trillion-7B-preview}
}

@misc{han2025trillion7btechnicalreport,
      title={Trillion 7B Technical Report}, 
      author={Sungjun Han and Juyoung Suk and Suyeong An and Hyungguk Kim and Kyuseok Kim and Wonsuk Yang and Seungtaek Choi and Jamin Shin},
      year={2025},
      eprint={2504.15431},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2504.15431}, 
}

Contact

For inquiries, please contact: info@trillionlabs.co