Text Generation
Safetensors
GGUF
English
dory
decision-making
lora
eider
bf16
nvfp4
llama-cpp
conversational
decision-model
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
- llama.cpp
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
- Pi
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"
Download gguf/gpu-boundaries.json from rdaum/decisive-dory: direct link, hf CLI and curl.
- Browser
- Download file 2.7 kB
-
https://huggingface.co/rdaum/decisive-dory/resolve/main/gguf/gpu-boundaries.json
- Command line
-
hf download hf://rdaum/decisive-dory/gguf/gpu-boundaries.json
-
curl -L -o gpu-boundaries.json https://huggingface.co/rdaum/decisive-dory/resolve/main/gguf/gpu-boundaries.json
2.7 kB
| { | |
| "alternate_answers": { | |
| "model": "eider-dory-decisions-gguf", | |
| "answers": { | |
| "colour": { | |
| "type": "choice", | |
| "choice": "blue", | |
| "probabilities": { | |
| "red": 6.588990763500258e-05, | |
| "blue": 0.999834129171347, | |
| "green": 9.998092101819787e-05 | |
| }, | |
| "confidence": 0.9997511937570203 | |
| }, | |
| "locked": { | |
| "type": "noul", | |
| "noul": 0.000527313024721127 | |
| }, | |
| "progress": { | |
| "type": "score", | |
| "score": 1.997173953179149, | |
| "legend": { | |
| "0": "Not started", | |
| "1": "Partly complete", | |
| "2": "Complete" | |
| }, | |
| "probabilities": { | |
| "0": 0.0010355160597244543, | |
| "1": 0.0007550147014019543, | |
| "2": 0.9982094692388735 | |
| }, | |
| "confidence": 0.9957609297687237 | |
| } | |
| }, | |
| "usage": { | |
| "input_tokens": 333, | |
| "output_tokens": 0 | |
| } | |
| }, | |
| "invalid_inputs": [ | |
| { | |
| "case": "one_choice", | |
| "status": 500, | |
| "response": { | |
| "error": { | |
| "code": 500, | |
| "message": "\n------------\nWhile executing CallExpression at line 8, column 23 in source:\n...| length > 64 -%}\u21b5 {{ raise_exception('Dory decisions require 2 to 64 options...\n ^\nError: Jinja Exception: Dory decisions require 2 to 64 options", | |
| "type": "server_error" | |
| } | |
| } | |
| }, | |
| { | |
| "case": "choices65", | |
| "status": 500, | |
| "response": { | |
| "error": { | |
| "code": 500, | |
| "message": "\n------------\nWhile executing CallExpression at line 8, column 23 in source:\n...| length > 64 -%}\u21b5 {{ raise_exception('Dory decisions require 2 to 64 options...\n ^\nError: Jinja Exception: Dory decisions require 2 to 64 options", | |
| "type": "server_error" | |
| } | |
| } | |
| }, | |
| { | |
| "case": "scores11", | |
| "status": 400, | |
| "response": { | |
| "error": { | |
| "code": 400, | |
| "message": "questions.q: \"criteria\" must be an array of 2 to 10 levels", | |
| "type": "invalid_request_error" | |
| } | |
| } | |
| }, | |
| { | |
| "case": "object_description", | |
| "status": 500, | |
| "response": { | |
| "error": { | |
| "code": 500, | |
| "message": "\n------------\nWhile executing CallExpression at line 54, column 27 in source:\n... is none) -%}\u21b5 {{ raise_exception('Option descriptions must be text, or n...\n ^\nError: Jinja Exception: Option descriptions must be text, or null for a choice", | |
| "type": "server_error" | |
| } | |
| } | |
| } | |
| ] | |
| } | |