Instructions to use MachineFi/Trio-Spark-DI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use MachineFi/Trio-Spark-DI 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 MachineFi/Trio-Spark-DI:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MachineFi/Trio-Spark-DI:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf MachineFi/Trio-Spark-DI:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MachineFi/Trio-Spark-DI: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 MachineFi/Trio-Spark-DI:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf MachineFi/Trio-Spark-DI: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 MachineFi/Trio-Spark-DI:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MachineFi/Trio-Spark-DI:Q4_K_M

Use Docker

docker model run hf.co/MachineFi/Trio-Spark-DI:Q4_K_M

LM Studio
Jan
Ollama
How to use MachineFi/Trio-Spark-DI with Ollama:
```
ollama run hf.co/MachineFi/Trio-Spark-DI:Q4_K_M
```
Unsloth Desktop

How to use MachineFi/Trio-Spark-DI with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MachineFi/Trio-Spark-DI:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "MachineFi/Trio-Spark-DI:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use MachineFi/Trio-Spark-DI with Docker Model Runner:
```
docker model run hf.co/MachineFi/Trio-Spark-DI:Q4_K_M
```

Lemonade

How to use MachineFi/Trio-Spark-DI with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MachineFi/Trio-Spark-DI:Q4_K_M

Run and chat with the model

lemonade run user.Trio-Spark-DI-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use MachineFi/Trio-Spark-DI with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MachineFi/Trio-Spark-DI: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 MachineFi/Trio-Spark-DI:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use MachineFi/Trio-Spark-DI with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MachineFi/Trio-Spark-DI: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 "MachineFi/Trio-Spark-DI: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"

Access Trio-Spark DI (research, non-commercial)

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

These weights are released under CC BY-NC 4.0 for research and evaluation only. Commercial use requires a separate license from MachineFi (or use the hosted Trio-Spark API).

Trio-Spark DI (research checkpoint)

Trio-Spark DI is a research checkpoint of MachineFi's Trio-Spark decision model, trained for the Decision Index typed-decision protocol: given a state and typed questions with explicit options, it returns a probability for every option.

Base model: Qwen/Qwen3.5-9B (Apache-2.0).
Weights: trio-spark-di-q8_0.gguf (Q8_0, about 9.5 GB, sha256 42edc59b…), the exact file we evaluated; checkpoint DI14. trio-spark-di-q4_k_m.gguf (Q4_K_M, about 5.6 GB) is the same checkpoint at 4 bits and scores lower.
Text only. The hosted Trio-Spark API (v1.3) is a different, production model with image and video input; this checkpoint is not what the API serves.

Decision Index 0.3 (public suite, self-run)

	Decision Index	Knowledge	Language	Retrieval	Tools	Arts
Trio-Spark DI (DI14, Q8_0), full public suite	56.74	40.4	60.0	59.4	79.3	43.9
Trio-Spark DI (DI14, Q4_K_M), full public suite	55.50	39.2	59.4	58.7	76.4	42.7

All 140,178 requests of the public 0.3 suite, run with the official kit against the server in serve/ (18 requests longer than 8,192 tokens refused, none truncated). The server is deterministic: the same request always gets the same answer. Submitted to the Decision Index leaderboard; the official score also includes private test sets and may differ.

Earlier checkpoints (Q4_K_M), on a 12,001-request stratified sample of the same suite:

	Decision Index	Knowledge	Language	Retrieval	Tools	Arts
Trio-Spark DI (DI14)	54.47	38.1	55.8	58.4	77.3	43.8
Trio-Spark DI (DI13, tag `di13`)	54.08	38.6	54.5	58.2	76.6	43.7

Running it

Trio-Spark DI is not a chat model. It answers through its decision prompt and reads a probability for every option from the model's next-letter scores, so plain chat prompts will not reproduce the scores above.

The /v1/systemone server used for the evaluation is in serve/: llama.cpp (release b11249, CUDA 12.8) with one slot per server and a small Python front end (standard library only) that evaluates the questions of a request in order and clears the slot between requests, so answers do not depend on what ran before. It is compatible with the Decision Index kit's http engine.

hf download MachineFi/Trio-Spark-DI --local-dir trio-spark-di
bash trio-spark-di/serve/setup.sh $PWD/trio-spark-di/trio-spark-di-q8_0.gguf
# then, from the Decision Index kit:
python -m decision_index pipeline --engine http --option base_url=http://127.0.0.1:8800 --option model=trio-spark --out runs/trio-spark-di

Limits: up to 8,192 tokens per decision prompt (longer requests are refused, never truncated); questions with more than 8 options are decided by an 8-way tournament (groups of 8, then a final over the group winners), so no option is dropped.

License

The weights are released under CC BY-NC 4.0: free for research and evaluation with attribution, no commercial use. For commercial use, use the hosted Trio-Spark API or contact MachineFi. The base model Qwen3.5-9B is Apache-2.0; its license and notice apply to the base weights (see NOTICE). Full license text: LICENSE.

The hosted, production model is documented at MachineFi/Trio-Spark.