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

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 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/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 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 6 days ago

Commit

04cfd60

verified ·

1 Parent(s): 5e71a7a

Remove links to retired format repositories and historical releases

Browse files

Files changed (1) hide show

README.md +0 -4

README.md CHANGED Viewed

@@ -58,10 +58,7 @@ All three formats contain the same fine-tuned `state-32k` model. They share this
 | Rank-8 LoRA adapter | [`adapter/`](adapter/README.md) | Standalone training changes for the original NVIDIA BF16 model. |
 BF16 is the merged full-precision release; NVFP4 and Q8_0 are its quantized exports.
-The existing [BF16 repository](https://huggingface.co/rdaum/eider-dory-1.0-4b-decisions-bf16)
-and [NVFP4 repository](https://huggingface.co/rdaum/eider-dory-1.0-4b-decisions-nvfp4) remain available.
-The previous weights and calibration remain available under the [`actions-48k`](https://huggingface.co/rdaum/eider-dory-1.0-4b-decisions-bf16/tree/actions-48k) tag.
 This release has **no fitted probability-calibration profile**.
 ## What this is for
@@ -265,7 +262,6 @@ The original unfine-tuned NVIDIA model was not rescored in this comparison.
 No calibration profile has been fitted for `state-32k`.
 The previous profile was removed from the current release because it belongs to different weights.
-It remains in the original [NVFP4 repository’s `actions-48k` tag](https://huggingface.co/rdaum/eider-dory-1.0-4b-decisions-nvfp4/tree/actions-48k) for reproducibility.
 The 1,024-question calibration split remains separate from training, checkpoint selection, and the new test.
 Uncalibrated softmax values are conditional scores over the permitted answers; they are not proven real-world outcome probabilities.

 | Rank-8 LoRA adapter | [`adapter/`](adapter/README.md) | Standalone training changes for the original NVIDIA BF16 model. |
 BF16 is the merged full-precision release; NVFP4 and Q8_0 are its quantized exports.
 This release has **no fitted probability-calibration profile**.
 ## What this is for
 No calibration profile has been fitted for `state-32k`.
 The previous profile was removed from the current release because it belongs to different weights.
 The 1,024-question calibration split remains separate from training, checkpoint selection, and the new test.
 Uncalibrated softmax values are conditional scores over the permitted answers; they are not proven real-world outcome probabilities.