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

Libraries

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

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("arcee-ai/Trinity-Mini-GGUF", dtype="auto")

llama-cpp-python

How to use arcee-ai/Trinity-Mini-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="arcee-ai/Trinity-Mini-GGUF",
	filename="Trinity-Mini-IQ2_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use arcee-ai/Trinity-Mini-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf arcee-ai/Trinity-Mini-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf arcee-ai/Trinity-Mini-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf arcee-ai/Trinity-Mini-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf arcee-ai/Trinity-Mini-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf arcee-ai/Trinity-Mini-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf arcee-ai/Trinity-Mini-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf arcee-ai/Trinity-Mini-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf arcee-ai/Trinity-Mini-GGUF:Q4_K_M

Use Docker

docker model run hf.co/arcee-ai/Trinity-Mini-GGUF:Q4_K_M

LM Studio
Jan

vLLM

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

Use Docker

docker model run hf.co/arcee-ai/Trinity-Mini-GGUF:Q4_K_M

SGLang

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

Ollama
How to use arcee-ai/Trinity-Mini-GGUF with Ollama:
```
ollama run hf.co/arcee-ai/Trinity-Mini-GGUF:Q4_K_M
```

Unsloth Studio new

How to use arcee-ai/Trinity-Mini-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for arcee-ai/Trinity-Mini-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for arcee-ai/Trinity-Mini-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for arcee-ai/Trinity-Mini-GGUF to start chatting

Pi new

How to use arcee-ai/Trinity-Mini-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf arcee-ai/Trinity-Mini-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "arcee-ai/Trinity-Mini-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use arcee-ai/Trinity-Mini-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf arcee-ai/Trinity-Mini-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default arcee-ai/Trinity-Mini-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use arcee-ai/Trinity-Mini-GGUF with Docker Model Runner:
```
docker model run hf.co/arcee-ai/Trinity-Mini-GGUF:Q4_K_M
```

Lemonade

How to use arcee-ai/Trinity-Mini-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull arcee-ai/Trinity-Mini-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Trinity-Mini-GGUF-Q4_K_M

List all available models

lemonade list

Trinity Mini GGUF

Trinity Mini is an Arcee AI 26B MoE model with 3B active parameters. It is the medium-sized model in our new Trinity family, a series of open-weight models for enterprise and tinkerers alike.

This model is tuned for reasoning, but in testing, it uses a similar total token count to competitive instruction-tuned models.

These are the GGUF files for running on llama.cpp powered platforms

Trinity Mini is trained on 10T tokens gathered and curated through a key partnership with Datology, building upon the excellent dataset we used on AFM-4.5B with additional math and code.

Training was performed on a cluster of 512 H200 GPUs powered by Prime Intellect using HSDP parallelism.

More details, including key architecture decisions, can be found on our blog here

Try it out now at chat.arcee.ai

Model Details

Model Architecture: AfmoeForCausalLM
Parameters: 26B, 3B active
Experts: 128 total, 8 active, 1 shared
Context length: 128k
Training Tokens: 10T
License: Apache 2.0
Recommended settings:
- temperature: 0.15
- top_k: 50
- top_p: 0.75
- min_p: 0.06

Benchmarks

Running our model

llama.cpp

Supported in llama.cpp release b7061

Download the latest llama.cpp release

llama-server -hf arcee-ai/Trinity-Mini-GGUF:q4_k_m \
  --temp 0.15 \
  --top-k 50 \
  --top-p 0.75
  --min-p 0.06

LM Studio

Supported in latest LM Studio runtime

Update to latest available, then verify your runtime by:

Click "Power User" at the bottom left
Click the green "Developer" icon at the top left
Select "LM Runtimes" at the top
Refresh the list of runtimes and verify that the latest is installed

Then, go to Model Search and search for arcee-ai/Trinity-Mini-GGUF, download your prefered size, and load it up in the chat

API

Trinity Mini is available today on openrouter:

https://openrouter.ai/arcee-ai/trinity-mini

curl -X POST "https://openrouter.ai/v1/chat/completions" \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "arcee-ai/trinity-mini",
    "messages": [
      {
        "role": "user",
        "content": "What are some fun things to do in New York?"
      }
    ]
  }'