Instructions to use bynn-intelligence-com/decisionnode-edge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bynn-intelligence-com/decisionnode-edge with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="bynn-intelligence-com/decisionnode-edge")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("bynn-intelligence-com/decisionnode-edge")
model = AutoModelForMultimodalLM.from_pretrained("bynn-intelligence-com/decisionnode-edge", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use bynn-intelligence-com/decisionnode-edge 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 bynn-intelligence-com/decisionnode-edge:Q8_0
# Run inference directly in the terminal:
llama cli -hf bynn-intelligence-com/decisionnode-edge:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf bynn-intelligence-com/decisionnode-edge:Q8_0
# Run inference directly in the terminal:
llama cli -hf bynn-intelligence-com/decisionnode-edge: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 bynn-intelligence-com/decisionnode-edge:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf bynn-intelligence-com/decisionnode-edge: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 bynn-intelligence-com/decisionnode-edge:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bynn-intelligence-com/decisionnode-edge:Q8_0

Use Docker

docker model run hf.co/bynn-intelligence-com/decisionnode-edge:Q8_0

Llama new
LM Studio
Jan

vLLM

How to use bynn-intelligence-com/decisionnode-edge with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bynn-intelligence-com/decisionnode-edge"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bynn-intelligence-com/decisionnode-edge",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bynn-intelligence-com/decisionnode-edge:Q8_0

SGLang

How to use bynn-intelligence-com/decisionnode-edge with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "bynn-intelligence-com/decisionnode-edge" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bynn-intelligence-com/decisionnode-edge",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "bynn-intelligence-com/decisionnode-edge" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bynn-intelligence-com/decisionnode-edge",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use bynn-intelligence-com/decisionnode-edge with Ollama:
```
ollama run hf.co/bynn-intelligence-com/decisionnode-edge:Q8_0
```
Unsloth Desktop
Docker Model Runner
How to use bynn-intelligence-com/decisionnode-edge with Docker Model Runner:
```
docker model run hf.co/bynn-intelligence-com/decisionnode-edge:Q8_0
```

Lemonade

How to use bynn-intelligence-com/decisionnode-edge with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bynn-intelligence-com/decisionnode-edge:Q8_0

Run and chat with the model

lemonade run user.decisionnode-edge-Q8_0

List all available models

lemonade list

Atomic Chat

bynn-intelligence commited on 5 days ago

Commit

82aab3f

verified ·

1 Parent(s): 62a295f

Model card: DecisionNode Flash for speed, DecisionNode for the highest accuracy

Browse files

Files changed (2) hide show

MANIFEST.sha256 +1 -1
README.md +3 -3

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@@ -2,7 +2,7 @@
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 2014f13a6fe1d9178c84cde47bb087a7a74adbe38650f1823a944077081e86d0  ./LICENSING.md
 e4dcac398e39a098e07f625ed239949296a2000d95dcc275cb0d8083ebb51dc9  ./NOTICE
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 c44489690e63adb3abd8c65ede675fadc5714126e3f866cbd292df824880cb77  ./assets/decisionnode-banner.png
 5febf5c3cb5843034f2251d959c475a4a629dec65bf3ea494b4fed0a3b8aa3aa  ./calibration.json
 28b44a18f60b7e3039c503971b6d194b51c723f0c8a62d56bd6ce7f7f34e9f87  ./config.json

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 2014f13a6fe1d9178c84cde47bb087a7a74adbe38650f1823a944077081e86d0  ./LICENSING.md
 e4dcac398e39a098e07f625ed239949296a2000d95dcc275cb0d8083ebb51dc9  ./NOTICE
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 c44489690e63adb3abd8c65ede675fadc5714126e3f866cbd292df824880cb77  ./assets/decisionnode-banner.png
 5febf5c3cb5843034f2251d959c475a4a629dec65bf3ea494b4fed0a3b8aa3aa  ./calibration.json
 28b44a18f60b7e3039c503971b6d194b51c723f0c8a62d56bd6ce7f7f34e9f87  ./config.json

README.md CHANGED Viewed

@@ -36,12 +36,12 @@ This repository contains DecisionNode Edge 0.9.1, the larger of the two open Dec
 ## The DecisionNode family
-DecisionNode Edge is an open-weight member of the DecisionNode family, for decisions on your own hardware. The hosted models lead the family: DecisionNode is the most accurate, and DecisionNode Flash is its fast, low-cost tier. Both are available through the [DecisionNode API](https://decisionnode.com/docs/api) with the full feature set, at very low cost.
 | Model | Availability | Decision accuracy | Best for |
 |---|---|---|---|
-| **DecisionNode** | Hosted API | **81.7%** | the highest accuracy, for the hardest decisions |
-| **DecisionNode Flash** | Hosted API | 78.3% | high accuracy at scale, at the lowest hosted cost |
 | DecisionNode Edge | Open weights | 74.2% | on-device and private deployment, from GPUs to laptops |
 | DecisionNode Edge Mini | Open weights | 69.2% | CPUs and constrained devices |

 ## The DecisionNode family
+DecisionNode Edge is an open-weight member of the DecisionNode family, for decisions on your own hardware. The hosted models lead the family. For speed, use [DecisionNode Flash](https://decisionnode.com/docs/models/flash): it answers in milliseconds and keeps high accuracy. When a decision needs the highest accuracy, use [DecisionNode](https://decisionnode.com/docs/models/decisionnode). Both are available through the [DecisionNode API](https://decisionnode.com/docs/api) with the full feature set, at very low cost.
 | Model | Availability | Decision accuracy | Best for |
 |---|---|---|---|
+| **[DecisionNode Flash](https://decisionnode.com/docs/models/flash)** | Hosted API | 78.3% | speed and accuracy together: decisions in milliseconds, at the lowest hosted cost |
+| **[DecisionNode](https://decisionnode.com/docs/models/decisionnode)** | Hosted API | **81.7%** | the highest accuracy, for the hardest decisions |
 | DecisionNode Edge | Open weights | 74.2% | on-device and private deployment, from GPUs to laptops |
 | DecisionNode Edge Mini | Open weights | 69.2% | CPUs and constrained devices |