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 evaluation/openrouter-comparison.json from rdaum/decisive-dory: direct link, hf CLI and curl.
- Browser
- Download file 13.8 kB
-
https://huggingface.co/rdaum/decisive-dory/resolve/main/evaluation/openrouter-comparison.json
- Command line
-
hf download hf://rdaum/decisive-dory/evaluation/openrouter-comparison.json
-
curl -L -o openrouter-comparison.json https://huggingface.co/rdaum/decisive-dory/resolve/main/evaluation/openrouter-comparison.json
13.8 kB
| { | |
| "protocol": { | |
| "endpoint": "https://openrouter.ai/api/alpha/decisions", | |
| "models": [ | |
| "typesafe/jev-1.14", | |
| "liquid/d1", | |
| "perplexity/pplx-decider-v1.2", | |
| "jaredpalmer/kev-4b" | |
| ], | |
| "test_data_sha256": "e2ca7413a306fc2b0e9e044aa685c94d5aa8e0def7cfcec6448941b7369ae625", | |
| "questions": 512, | |
| "protocol": "One request per question. Identical state, question instructions, answer meanings, and option order; no reference targets sent. Native provider prompts and calibration remain provider-controlled. No prompt optimization or fitting.", | |
| "concurrency": "Four models concurrently, one request at a time per model. HTTP latency includes network/provider overhead and is not local GPU compute latency.", | |
| "probability_rounding": "Returned distributions within 0.02 of unit mass are renormalized before Brier scoring; raw API responses are retained. Argmax option order breaks probability ties.", | |
| "cost_limit_usd": 1.0, | |
| "date": "2026-10-21" | |
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| "max_probability_sum_error": 4.440892098500626e-16, | |
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| "liquid/d1-20260930" | |
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| "requested_model": "jaredpalmer/kev-4b", | |
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| "jaredpalmer/kev-4b-20260924" | |
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| "errors": {}, | |
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| "local_results": { | |
| "Decisive Dory \u2014 Q8_0 / llama.cpp": { | |
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| } | |
| }, | |
| "model_sha256": "bb36b8ed263ce633649ccfad9c6e1dc065e9c3d333624fc27028f496089a38b8", | |
| "loops": 2, | |
| "prompt_parity_questions": 512, | |
| "command": [ | |
| "llama-server", | |
| "--model", | |
| "dory-decisions-Q8_0.gguf", | |
| "--alias", | |
| "dory-gguf-eval", | |
| "--host", | |
| "127.0.0.1", | |
| "--port", | |
| "18091", | |
| "--ctx-size", | |
| "4096", | |
| "--parallel", | |
| "1", | |
| "--threads", | |
| "4", | |
| "--threads-batch", | |
| "4", | |
| "--batch-size", | |
| "512", | |
| "--ubatch-size", | |
| "512", | |
| "--fit", | |
| "off", | |
| "--gpu-layers", | |
| "all", | |
| "--no-warmup" | |
| ] | |
| }, | |
| "Decisive Dory \u2014 NVFP4 / Eider": { | |
| "questions": 512, | |
| "modal_accuracy": 0.771484375, | |
| "brier": 0.26463106057883456, | |
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| "by_kind": { | |
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| "cross_entropy": 1.1801933058267577, | |
| "mean_request_ms": 75.06443578865827, | |
| "median_request_ms": 72.3452023230493 | |
| }, | |
| "noul": { | |
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| "modal_accuracy": 0.9574468085106383, | |
| "brier": 0.09152335422286362, | |
| "cross_entropy": 0.2881069286288201, | |
| "mean_request_ms": 67.15274397581022, | |
| "median_request_ms": 67.46977800503373 | |
| }, | |
| "score": { | |
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| "cross_entropy": 1.3499867336653204, | |
| "mean_request_ms": 95.45610623307905, | |
| "median_request_ms": 77.96838786453009 | |
| } | |
| }, | |
| "by_source": { | |
| "helpsteer2": { | |
| "questions": 128, | |
| "modal_accuracy": 0.5078125, | |
| "brier": 0.22775570941129228, | |
| "cross_entropy": 1.5323848707632575, | |
| "mean_request_ms": 107.58943439577706, | |
| "median_request_ms": 97.03774400986731 | |
| }, | |
| "planning": { | |
| "questions": 192, | |
| "modal_accuracy": 0.9635416666666666, | |
| "brier": 0.155754142490034, | |
| "cross_entropy": 0.834348251030471, | |
| "mean_request_ms": 74.49704928634067, | |
| "median_request_ms": 73.17394809797406 | |
| }, | |
| "scone": { | |
| "questions": 192, | |
| "modal_accuracy": 0.7552083333333334, | |
| "brier": 0.3980915461126633, | |
| "cross_entropy": 1.0110216781175627, | |
| "mean_request_ms": 68.87362862714023, | |
| "median_request_ms": 68.2264359202236 | |
| } | |
| }, | |
| "model_sha256": "62bc20a6fbafaf157154650dfb4e9ec32a19d67e8e1f60ddcc3318fd37d6ccb4", | |
| "loops": 2, | |
| "calibrated": false, | |
| "binary_sha256": "935335c9df451b4b9108d65fc46882abbfd7a0b6cb20b3ba76f4e8c42fa7ab0c", | |
| "quantization_recipe": "NVIDIA Model Optimizer local_hessian", | |
| "calibration_examples": 256 | |
| } | |
| }, | |
| "scope": "512 test questions withheld from this fine-tune. Task families are represented in its training. Provider training overlap is unknown. This is not an official source benchmark or a general model ranking.", | |
| "scoring": "Accuracy accepts any answer tied for highest target probability. Brier is the sum of squared differences between predicted and target probabilities, averaged over questions.", | |
| "cost_scope": "Cost covers scored requests and calls made during the final resumed run. Earlier failed-response and diagnostic probe costs are excluded." | |
| } | |