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 Settings

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
```

juyoung-trl commited on Jul 22, 2025

Commit

60d2bde

verified ·

1 Parent(s): d5d5f3a

Upload folder using huggingface_hub

Browse files

Files changed (3) hide show

.gitattributes +1 -0
README.md +11 -1
assets/pareto-tri.png +3 -0

.gitattributes CHANGED Viewed

@@ -36,3 +36,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 assets/Signiture_Black_White_BG_resized.jpg filter=lfs diff=lfs merge=lfs -text
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+assets/pareto-tri.png filter=lfs diff=lfs merge=lfs -text

README.md CHANGED Viewed

@@ -9,7 +9,11 @@ language:
 - ja
 pipeline_tag: text-generation
 library_name: transformers
----
 # Tri-7B
@@ -29,6 +33,12 @@ We introduce **Tri-7B**, the next generation model following Trillion-7B-preview
 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

 - ja
 pipeline_tag: text-generation
 library_name: transformers
+extra_gated_fields:
+  Full Name: text
+  Email: text
+  Organization: text
+ㄴ---
 # Tri-7B
 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.
+<p align="center">
+<img src="assets/pareto-tri.png" alt="Average Performance vs. Approximate Training FLOPs" width="700">
+</p>
 ### Model Specifications
 #### Tri-7B

assets/pareto-tri.png ADDED Viewed

Git LFS Details

SHA256: 8766e708c45cde9deca6797d04b769fa751a522b565ef492c43dd35a6a34b939
Pointer size: 131 Bytes
Size of remote file: 277 kB