Trillion-7B-preview

Introduction

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

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.

• 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.

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!
"""

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

• *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

• Trillion-7B-preview
• LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct
• google/gemma-2-9b-it
• meta-llama/Llama-3.1-8B-Instruct
• Qwen/Qwen2.5-7B-Instruct
• upstage/SOLAR-10.7B-Instruct-v1.0
• mistralai/Mistral-7B-Instruct-v0.3

General Reasoning and Factuality

Coding

Mathematical Reasoning

Instruction Following and Chat

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}
}

Contact

For inquiries, please contact: info@trillionlabs.co