Instructions to use arcee-ai/Trinity-Mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
- 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
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8d1ea33 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | # 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"] |