Instructions to use trillionlabs/Tri-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use trillionlabs/Tri-7B with Transformers:

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

pipe = pipeline("text-generation", model="trillionlabs/Tri-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("trillionlabs/Tri-7B")
model = AutoModelForCausalLM.from_pretrained("trillionlabs/Tri-7B")
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

vLLM

How to use trillionlabs/Tri-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "trillionlabs/Tri-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "trillionlabs/Tri-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/trillionlabs/Tri-7B

SGLang

How to use trillionlabs/Tri-7B 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 "trillionlabs/Tri-7B" \
    --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": "trillionlabs/Tri-7B",
		"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 "trillionlabs/Tri-7B" \
        --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": "trillionlabs/Tri-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use trillionlabs/Tri-7B with Docker Model Runner:
```
docker model run hf.co/trillionlabs/Tri-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Tri-7B

Introduction

We introduce Tri-7B, the next generation model following Trillion-7B-preview, that continues to push the boundaries of efficient training while achieving exceptional performance at the 7B parameter scale.

Average Performance vs. Approximate Training FLOPs

Key Highlights

Enhanced Reasoning: Modified training dataset mixture specifically optimized for reasoning capabilities
Advanced Post-Training: Significantly improved RL training pipeline focusing on mathematical reasoning and everyday usage
Extended Context: Supports up to 32K context length for long-form understanding
Multi-lingual: Specially optimized for Korean, English, and Japanese.

Our Tri-7B model represents a significant advancement over Trillion-7B-preview, achieving substantial performance improvements across all evaluated domains while maintaining the same efficient parameter count.

Model Specifications

Tri-7B

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: 32,768
Vocab Size: 128,256

Quickstart

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

Tri-7B Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "trillionlabs/Tri-7B"

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

prompt = "Explain the concept of quantum computing in simple terms."
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,
    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)

Tri-7B is also available with vLLM and SGLang!

# vLLM
vllm serve trillionlabs/Tri-7B --dtype bfloat16 --max-model-len 32768

# vLLM with custom options
vllm serve trillionlabs/Tri-7B \
    --dtype bfloat16 \
    --max-model-len 32768 \
    --gpu-memory-utilization 0.95 \
    --port 8000

# SGLang
python3 -m sglang.launch_server --model-path trillionlabs/Tri-7B --dtype bfloat16

# SGLang with custom options
python3 -m sglang.launch_server \
    --model-path trillionlabs/Tri-7B \
    --dtype bfloat16 \
    --context-length 32768 \
    --port 30000 \
    --host 0.0.0.0

Evaluation

We evaluated Tri-7B across a comprehensive suite of benchmarks assessing general reasoning, knowledge recall, coding abilities, mathematical reasoning, and instruction-following capabilities. Compared to our previous generation model Trillion-7B-preview, Tri-7B achieves significant gains across all domains.

Full evaluation settings

Benchmark	Language	Evaluation Setting	Metric
General Reasoning and Factuality
• HellaSwag	English	0-shot	accuracy
• ARC:C	English	0-shot	accuracy
• HAERAE	Korean	3-shot	accuracy
• CLIcK	Korean	0-shot	accuracy
• KoBEST	Korean	5-shot	accuracy
Knowledge and Reasoning
• KMMLU	Korean	5-shot (0-shot, CoT)	accuracy
• MMLU	English	5-shot (0-shot, CoT)	accuracy
• Global-MMLU-Lite-ja	English	5-shot	accuracy
Coding
• HumanEval	English	0-shot	pass@1
• MBPPPlus	English	0-shot	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)	LLM score
• KO-MT-Bench	Korean	LLM-as-a-judge (gpt-4o)	LLM score
• systemIFEval	English	0-shot	strict-average

*Note that koIFEval, systemIFEval, and KoRuler are our in-house evaluation benchmarks adapted for Korean to better assess model capabilities in Korean language tasks.
**Note that MT-Bench, KO-MT-Bench, and LogicKor use a 10-point scale.

Benchmark Results

Models compared:

Tri-7B (Next Generation)
Trillion-7B-preview (Previous Generation)

General Reasoning and Factuality

Benchmark	Tri-7B	Trillion-7B-preview	Improvement
HellaSwag	59.52	58.94	+0.58
ARC:C	58.28	54.44	+3.84
HAERAE	82.49	80.02	+2.47
KoBEST	82.72	79.61	+3.11
CLIcK	64.43	60.41	+4.02
KMMLU	51.74 (53.51)	48.09	+3.65
MMLU	68.16 (74.67)	63.52	+4.64
Global-MMLU-Lite-ja	59.25	60.75	-1.50

Coding

Benchmark	Tri-7B	Trillion-7B-preview	Improvement
HumanEval	53.66	55.48	-1.82
MBPPPlus	64.29	58.99	+5.30

Mathematical Reasoning

Benchmark	Tri-7B	Trillion-7B-preview	Improvement
GSM8k	77.94	72.25	+5.69
MATH	49.40	32.70	+16.70
GPQA	34.15	32.81	+1.34
HRM8k	39.08	30.10	+8.98

Instruction Following and Chat

Benchmark	Tri-7B	Trillion-7B-preview	Improvement
IFEval	79.26	79.13	+0.13
koIFEval	76.63	66.58	+10.05
MT-Bench	7.82	6.53	+1.29
KO-MT-Bench	7.64	6.27	+1.37
systemIFEval	66.43	27.28	+39.15

Limitations

Language Support: The model is optimized for English, Korean, and Japanese. Usage with other languages may result in degraded performance.
Knowledge Cutoff: The model's information is limited to data available up to Febuary, 2025.