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

c55e78b

verified ·

1 Parent(s): 22cf894

Model card: the hosted models for the highest accuracy

Browse files

Files changed (2) hide show

MANIFEST.sha256 +1 -1
README.md +8 -8

MANIFEST.sha256 CHANGED Viewed

@@ -2,7 +2,7 @@
 50cbab8a892c5f2993b8c7351a99182507472def3b1374558308605d99b86b32  ./LICENSE-base
 2014f13a6fe1d9178c84cde47bb087a7a74adbe38650f1823a944077081e86d0  ./LICENSING.md
 e4dcac398e39a098e07f625ed239949296a2000d95dcc275cb0d8083ebb51dc9  ./NOTICE
-28c7af7e249d0e7bc648f9a83e42740dc37487f748eb12b457b53a6f2fba61e6  ./README.md
 c44489690e63adb3abd8c65ede675fadc5714126e3f866cbd292df824880cb77  ./assets/decisionnode-banner.png
 5febf5c3cb5843034f2251d959c475a4a629dec65bf3ea494b4fed0a3b8aa3aa  ./calibration.json
 28b44a18f60b7e3039c503971b6d194b51c723f0c8a62d56bd6ce7f7f34e9f87  ./config.json

 50cbab8a892c5f2993b8c7351a99182507472def3b1374558308605d99b86b32  ./LICENSE-base
 2014f13a6fe1d9178c84cde47bb087a7a74adbe38650f1823a944077081e86d0  ./LICENSING.md
 e4dcac398e39a098e07f625ed239949296a2000d95dcc275cb0d8083ebb51dc9  ./NOTICE
+a85c3b1edbf52508e03c489415f1484b00b74325d0fbddeb92c3d6f6127ee4d9  ./README.md
 c44489690e63adb3abd8c65ede675fadc5714126e3f866cbd292df824880cb77  ./assets/decisionnode-banner.png
 5febf5c3cb5843034f2251d959c475a4a629dec65bf3ea494b4fed0a3b8aa3aa  ./calibration.json
 28b44a18f60b7e3039c503971b6d194b51c723f0c8a62d56bd6ce7f7f34e9f87  ./config.json

README.md CHANGED Viewed

@@ -22,7 +22,7 @@ DecisionNode Edge is an open-weight System One decision model from [Bynn Intelli
 A System One model is a class of AI model that returns typed, probabilistic decisions from a fixed answer space instead of generating text. Given a piece of state and a set of questions defined in code, it answers each one with a schema-valid value such as a chosen option, a score, or a probability, and then stops.
-This repository contains DecisionNode Edge 0.9.1, the larger of the two DecisionNode Edge models. [DecisionNode Edge Mini](https://huggingface.co/bynn-intelligence-com/decisionnode-edge-mini) is a 0.85-billion-parameter sibling for CPUs and constrained devices.
 ## Highlights
@@ -36,14 +36,14 @@ This repository contains DecisionNode Edge 0.9.1, the larger of the two Decision
 ## The DecisionNode family
-DecisionNode Edge is the open-weight member of the DecisionNode family. The hosted models, DecisionNode and DecisionNode Flash, offer the highest accuracy and the full feature set through the [DecisionNode API](https://decisionnode.com/docs/api), at very low cost.
-| Model | Availability | Decision accuracy |
-|---|---|---|
-| DecisionNode | Hosted API | 81.7% |
-| DecisionNode Flash | Hosted API | 78.3% |
-| DecisionNode Edge | Open weights | 74.2% |
-| DecisionNode Edge Mini | Open weights | 69.2% |
 All four models are measured on the same 15-benchmark evaluation suite (see Evaluation).

 A System One model is a class of AI model that returns typed, probabilistic decisions from a fixed answer space instead of generating text. Given a piece of state and a set of questions defined in code, it answers each one with a schema-valid value such as a chosen option, a score, or a probability, and then stops.
+This repository contains DecisionNode Edge 0.9.1, the larger of the two open DecisionNode Edge models. [DecisionNode Edge Mini](https://huggingface.co/bynn-intelligence-com/decisionnode-edge-mini) is a 0.85-billion-parameter sibling for CPUs and constrained devices. When accuracy matters most, use the hosted models on the [DecisionNode API](https://decisionnode.com/docs/api): DecisionNode, the most accurate model in the family, or DecisionNode Flash.
 ## Highlights
 ## 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 |
 All four models are measured on the same 15-benchmark evaluation suite (see Evaluation).