Instructions to use rdaum/decisive-dory with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use rdaum/decisive-dory 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 rdaum/decisive-dory:Q8_0
# Run inference directly in the terminal:
llama cli -hf rdaum/decisive-dory:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf rdaum/decisive-dory:Q8_0
# Run inference directly in the terminal:
llama cli -hf rdaum/decisive-dory:Q8_0

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf rdaum/decisive-dory:Q8_0
# Run inference directly in the terminal:
./build/bin/llama cli -hf rdaum/decisive-dory:Q8_0

Use Docker

docker model run hf.co/rdaum/decisive-dory:Q8_0

Llama new
LM Studio
Jan

vLLM

How to use rdaum/decisive-dory with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/rdaum/decisive-dory:Q8_0

Ollama
How to use rdaum/decisive-dory with Ollama:
```
ollama run hf.co/rdaum/decisive-dory:Q8_0
```
Unsloth Desktop

How to use rdaum/decisive-dory with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf rdaum/decisive-dory:Q8_0

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": "rdaum/decisive-dory:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use rdaum/decisive-dory with Docker Model Runner:
```
docker model run hf.co/rdaum/decisive-dory:Q8_0
```

Lemonade

How to use rdaum/decisive-dory with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rdaum/decisive-dory:Q8_0

Run and chat with the model

lemonade run user.decisive-dory-Q8_0

List all available models

lemonade list

Hermes Agent

How to use rdaum/decisive-dory with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf rdaum/decisive-dory:Q8_0

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 rdaum/decisive-dory:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use rdaum/decisive-dory with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf rdaum/decisive-dory:Q8_0

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 "rdaum/decisive-dory:Q8_0" \
  --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"

rdaum commited on 15 days ago

Commit

634ccd8

verified ·

1 Parent(s): 83e633b

Clarify PyTorch training and Eider inference roles

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -39,7 +39,7 @@ Supply context, a question, and the allowed answers. The model scores those answ
 Use it for workflow routing, tool-call validation, evidence checks, rubric scoring, and action selection.
 Questions and answer meanings are supplied at request time, rather than fixed to a single classification task.
-The model was trained and evaluated with [Eider](https://github.com/rdaum/eider), a Rust/CUDA inference server for NVIDIA DGX Spark.
 The GGUF also works with llama.cpp's native decision endpoint, using its Dory support branch as described below.
 ## Model and formats
@@ -255,7 +255,7 @@ Image and audio inputs are unsupported.
 ## Attribution and provenance
 NVIDIA supplies the base model; the linked dataset authors supply the training sources.
-[Eider](https://github.com/rdaum/eider) supplies the training tooling and evaluated inference implementation.
 This is an independent derivative under [OpenMDW-1.1](LICENSE); see [NOTICE](NOTICE).
 [BF16 provenance](provenance.json), [NVFP4 provenance](nvfp4/provenance.json), and [file hashes](formats.json) identify the source weights and adapter.

 Use it for workflow routing, tool-call validation, evidence checks, rubric scoring, and action selection.
 Questions and answer meanings are supplied at request time, rather than fixed to a single classification task.
+The model was fine-tuned with PyTorch and evaluated using [Eider](https://github.com/rdaum/eider), a Rust/CUDA inference server.
 The GGUF also works with llama.cpp's native decision endpoint, using its Dory support branch as described below.
 ## Model and formats
 ## Attribution and provenance
 NVIDIA supplies the base model; the linked dataset authors supply the training sources.
+[Eider](https://github.com/rdaum/eider) supplies the evaluated inference implementation.
 This is an independent derivative under [OpenMDW-1.1](LICENSE); see [NOTICE](NOTICE).
 [BF16 provenance](provenance.json), [NVFP4 provenance](nvfp4/provenance.json), and [file hashes](formats.json) identify the source weights and adapter.