Instructions to use arcee-ai/Trinity-Mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use arcee-ai/Trinity-Mini with Transformers:

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

pipe = pipeline("text-generation", model="arcee-ai/Trinity-Mini", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("arcee-ai/Trinity-Mini", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("arcee-ai/Trinity-Mini", trust_remote_code=True)
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 arcee-ai/Trinity-Mini with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/arcee-ai/Trinity-Mini

SGLang

How to use arcee-ai/Trinity-Mini 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 "arcee-ai/Trinity-Mini" \
    --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": "arcee-ai/Trinity-Mini",
		"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 "arcee-ai/Trinity-Mini" \
        --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": "arcee-ai/Trinity-Mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use arcee-ai/Trinity-Mini with Docker Model Runner:
```
docker model run hf.co/arcee-ai/Trinity-Mini
```

Trinity-Mini / Dockerfile

kyleone

Upload Dockerfile

b95cafc verified 7 months ago

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	# Build frontend
	FROM node:18 as frontend-build
	WORKDIR /app
	COPY frontend/package*.json ./
	RUN npm install
	COPY frontend/ ./

	RUN npm run build

	# Build backend
	FROM python:3.12-slim
	WORKDIR /app

	# Create non-root user
	RUN useradd -m -u 1000 user

	# Install poetry
	RUN pip install poetry

	# Create and configure cache directory
	RUN mkdir -p /app/.cache && \
	chown -R user:user /app

	# Copy and install backend dependencies
	COPY backend/pyproject.toml backend/poetry.lock* ./
	RUN poetry config virtualenvs.create false \
	&& poetry install --no-interaction --no-ansi --no-root --only main

	# Copy backend code
	COPY backend/ .

	# Install Node.js and npm
	RUN apt-get update && apt-get install -y \
	curl \
	netcat-openbsd \
	&& curl -fsSL https://deb.nodesource.com/setup_18.x \| bash - \
	&& apt-get install -y nodejs \
	&& rm -rf /var/lib/apt/lists/*

	# Copy frontend server and build
	COPY --from=frontend-build /app/build ./frontend/build
	COPY --from=frontend-build /app/package*.json ./frontend/
	COPY --from=frontend-build /app/server.js ./frontend/

	# Install frontend production dependencies
	WORKDIR /app/frontend
	RUN npm install --production
	WORKDIR /app

	# Environment variables
	ENV HF_HOME=/app/.cache \
	HF_DATASETS_CACHE=/app/.cache \
	INTERNAL_API_PORT=7861 \
	PORT=7860 \
	NODE_ENV=production

	# Note: HF_TOKEN should be provided at runtime, not build time
	USER user
	EXPOSE 7860

	# Start both servers with wait-for
	CMD ["sh", "-c", "uvicorn app.asgi:app --host 0.0.0.0 --port 7861 & while ! nc -z localhost 7861; do sleep 1; done && cd frontend && npm run serve"]