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 5 days ago

Commit

9327770

verified ·

1 Parent(s): 3adea3f

Add unadapted Dory gameplay reference

Browse files

Files changed (2) hide show

README.md +2 -0
evaluation/jevman-comparison.json +56 -1

README.md CHANGED Viewed

@@ -233,6 +233,8 @@ Each local game starts a fresh server. Server startup is excluded from gameplay
 All completed replays passed the upstream verifier.
 Jev scores higher in this sample. Dory has similar scores through both runtimes, with lower request latency through Eider.
 These repeated real-time games do not establish performance across other maps or games.
 [Per-game results and configuration](evaluation/jevman-comparison.json) provide the measurements behind the chart.

 All completed replays passed the upstream verifier.
 Jev scores higher in this sample. Dory has similar scores through both runtimes, with lower request latency through Eider.
+Unadapted Dory with the same NVFP4 recipe scored 1,097 mean and 1,145 median over six games.
 These repeated real-time games do not establish performance across other maps or games.
 [Per-game results and configuration](evaluation/jevman-comparison.json) provide the measurements behind the chart.

evaluation/jevman-comparison.json CHANGED Viewed

@@ -228,5 +228,60 @@
     }
   ],
   "eider_weights_sha256": "62bc20a6fbafaf157154650dfb4e9ec32a19d67e8e1f60ddcc3318fd37d6ccb4",
-  "llama_q8_weights_sha256": "bb36b8ed263ce633649ccfad9c6e1dc065e9c3d333624fc27028f496089a38b8"
 }

     }
   ],
   "eider_weights_sha256": "62bc20a6fbafaf157154650dfb4e9ec32a19d67e8e1f60ddcc3318fd37d6ccb4",
+  "llama_q8_weights_sha256": "bb36b8ed263ce633649ccfad9c6e1dc065e9c3d333624fc27028f496089a38b8",
+  "base_reference": {
+    "model": "NVIDIA Dory 1.0 4B (unadapted)",
+    "route": "NVFP4 \u00b7 Eider",
+    "games": [
+      {
+        "score": 1490,
+        "survived_seconds": 48.98480000000033,
+        "calls": 115,
+        "latency_ms_sum": 22525,
+        "replay_sha256": "f06d720ed6fa6881a82cbb58ab0da4446e9553ee620b3d82047872e52577a27c"
+      },
+      {
+        "score": 680,
+        "survived_seconds": 35.804200000000534,
+        "calls": 82,
+        "latency_ms_sum": 14881,
+        "replay_sha256": "6eae5fd6214d64446a285712a0e4b76a736edf90b9ea78ebedce31fdb3b1b8c1"
+      },
+      {
+        "score": 1210,
+        "survived_seconds": 36.868400000000015,
+        "calls": 83,
+        "latency_ms_sum": 13773,
+        "replay_sha256": "64a36d3e4c9d8c4be8e20caac33c39478f84ca81190151d9538022ef3e744935"
+      },
+      {
+        "score": 1080,
+        "survived_seconds": 32.14370000000079,
+        "calls": 62,
+        "latency_ms_sum": 10051,
+        "replay_sha256": "4090ddef29ed6b35294af7153ae6eb46de68a7c2ec60f381c1b5b1ec279c97e7"
+      },
+      {
+        "score": 1490,
+        "survived_seconds": 42.16530000000059,
+        "calls": 90,
+        "latency_ms_sum": 14641,
+        "replay_sha256": "155a735437f3788abbddee63382697b0b7b0d47ba4ea0aa77e5f07a2f13254d0"
+      },
+      {
+        "score": 630,
+        "survived_seconds": 33.85650000000001,
+        "calls": 75,
+        "latency_ms_sum": 13615,
+        "replay_sha256": "32499a938cc6265eae1f45f313d5962bdb79d8a07e14ee5bcec4d45766e045ea"
+      }
+    ],
+    "game_count": 6,
+    "mean_score": 1096.6666666666667,
+    "median_score": 1145.0,
+    "mean_decision_ms": 176.5009861932939,
+    "scheduler_errors": 0,
+    "weights_sha256": "2dc8446f2c52d3249eac5bd2154f1f4a8f206152e3b66d7077965b9ad991bbf7",
+    "quantization": "Same ModelOpt recipe and calibration examples as Decisive Dory"
+  }
 }