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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "trillionlabs/Tri-7B-Base"
# 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-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

Tri-7B-Base

Introduction

We present Tri-7B-Base, a foundation language model that serves as the pre-trained base for our Tri-7B model family. This model represents our commitment to efficient training while establishing a strong foundation for downstream fine-tuning and adaptation.

Key Features

Foundation Architecture: State-of-the-art transformer architecture optimized for efficiency
Multi-lingual Foundation: Pre-trained on diverse data in Korean, English, and Japanese
Efficient Training: Optimized training methodology for computational efficiency

Model Specifications

Tri-7B-Base

Type: Causal Language Model
Training Stage: Pre-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
Vocab Size: 128,128

Use Cases

As a base model, Tri-7B-Base is designed to serve as a foundation for various downstream applications:

Fine-tuning: Adapt to specific domains or tasks
Instruction Tuning: Create chat or assistant models
Domain Specialization: Customize for specific industries or use cases
Research: Explore model behaviors and capabilities
Language Generation: General text completion and generation tasks

Limitations

Base Model Nature: This is a pre-trained base model without instruction tuning or alignment. For chat or assistant capabilities, consider fine-tuned variants.
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 February, 2025.
Generation Quality: As a base model, outputs may require post-processing or filtering for production use cases.