Instructions to use arcee-ai/Trinity-Mini-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use arcee-ai/Trinity-Mini-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -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 serve -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
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
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 serve -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
- 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
- Hermes Agent
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 serve -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
- Atomic Chat
- OpenClaw
How to use arcee-ai/Trinity-Mini-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf arcee-ai/Trinity-Mini-GGUF:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "arcee-ai/Trinity-Mini-GGUF:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
File size: 3,745 Bytes
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license: other
language:
- en
- es
- fr
- de
- it
- pt
- ru
- ar
- hi
- ko
- zh
library_name: transformers
base_model:
- arcee-ai/Trinity-Mini
base_model_relation: quantized
pipeline_tag: text-generation
license_link: LICENSE
license_name: openmdw-1.1
---
<div align="center">
<picture>
<img
src="https://cdn-uploads.huggingface.co/production/uploads/6435718aaaef013d1aec3b8b/i-v1KyAMOW_mgVGeic9WJ.png"
alt="Arcee Trinity Mini"
style="max-width: 100%; height: auto;"
>
</picture>
</div>
# 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](https://www.datologyai.com/), building upon the excellent dataset we used on [AFM-4.5B](https://huggingface.co/arcee-ai/AFM-4.5B) with additional math and code.
Training was performed on a cluster of 512 H200 GPUs powered by [Prime Intellect](https://www.primeintellect.ai/) using HSDP parallelism.
More details, including key architecture decisions, can be found on our blog [here](https://www.arcee.ai/blog/the-trinity-manifesto)
Try it out now at [chat.arcee.ai](http://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:** [OpenMDW-1.1](https://huggingface.co/arcee-ai/Trinity-Mini#license)
* **Recommended settings:**
* temperature: 0.15
* top_k: 50
* top_p: 0.75
* min_p: 0.06
***
## Benchmarks

<div align="center">
<picture>
<img src="https://cdn-uploads.huggingface.co/production/uploads/6435718aaaef013d1aec3b8b/sSVjGNHfrJKmQ6w8I18ek.png" style="background-color:ghostwhite;padding:5px;" width="17%" alt="Powered by Datology">
</picture>
</div>
### Running our model
- [llama.cpp](https://huggingface.co/arcee-ai/Trinity-Mini#llamacpp)
- [LM Studio](https://huggingface.co/arcee-ai/Trinity-Mini#lm-studio)
- [API](https://huggingface.co/arcee-ai/Trinity-Mini#api)
## llama.cpp
Supported in llama.cpp release b7061
Download the latest [llama.cpp release](https://github.com/ggml-org/llama.cpp/releases)
```
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:
1. Click "Power User" at the bottom left
2. Click the green "Developer" icon at the top left
3. Select "LM Runtimes" at the top
4. 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?"
}
]
}'
```
## License
Trinity-Mini is released under the OpenMDW-1.1 license. |