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

Libraries

How to use bynn-intelligence-com/decisionnode-edge-mini 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-mini")

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

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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

Use Docker

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

Llama new
LM Studio
Jan

vLLM

How to use bynn-intelligence-com/decisionnode-edge-mini 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-mini"
# 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-mini",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use bynn-intelligence-com/decisionnode-edge-mini 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-mini" \
    --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-mini",
		"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-mini" \
        --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-mini",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

Lemonade

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

Pull the model

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

Run and chat with the model

lemonade run user.decisionnode-edge-mini-Q8_0

List all available models

lemonade list

Atomic Chat

bynn-intelligence commited on 5 days ago

Commit

d2c7dff

verified ·

1 Parent(s): 3073dd4

Model card: the quick-start answer from the published weights

Browse files

Files changed (2) hide show

MANIFEST.sha256 +1 -1
README.md +6 -6

MANIFEST.sha256 CHANGED Viewed

@@ -2,7 +2,7 @@
 50cbab8a892c5f2993b8c7351a99182507472def3b1374558308605d99b86b32  ./LICENSE-base
 2014f13a6fe1d9178c84cde47bb087a7a74adbe38650f1823a944077081e86d0  ./LICENSING.md
 a23ef088ac75a7835adb29e8beecf021dd800ab0de680f0749854ad96d99ab9d  ./NOTICE
-c9c6f60cedeb6018369dbf7c769d48555833f09bd2bd385aac63c326e1ae8734  ./README.md
 c44489690e63adb3abd8c65ede675fadc5714126e3f866cbd292df824880cb77  ./assets/decisionnode-banner.png
 930c4ffef71c486cc2f3f85f034dac6072457f2ce98f24fd12eece88446cf6d6  ./calibration.json
 e2bebad8e9bbbb35aee3d61bbdc07be43b0e55345862a2f56ba194775bf77023  ./config.json

 50cbab8a892c5f2993b8c7351a99182507472def3b1374558308605d99b86b32  ./LICENSE-base
 2014f13a6fe1d9178c84cde47bb087a7a74adbe38650f1823a944077081e86d0  ./LICENSING.md
 a23ef088ac75a7835adb29e8beecf021dd800ab0de680f0749854ad96d99ab9d  ./NOTICE
+650678158a88295784d612a638794c465ebe23318f1ce0333876ee75fa034cc6  ./README.md
 c44489690e63adb3abd8c65ede675fadc5714126e3f866cbd292df824880cb77  ./assets/decisionnode-banner.png
 930c4ffef71c486cc2f3f85f034dac6072457f2ce98f24fd12eece88446cf6d6  ./calibration.json
 e2bebad8e9bbbb35aee3d61bbdc07be43b0e55345862a2f56ba194775bf77023  ./config.json

README.md CHANGED Viewed

@@ -155,10 +155,10 @@ response = model.decide({
             "instructions": "How urgent is this?",
             "criteria": ["routine", "today", "urgent", "critical"],
         },
-        "escalate": {"type": "truth", "instructions": "Escalate to a human now?"},
     },
 })
-print(response["answers"]["route"]["choice"], response["answers"]["escalate"]["truth"])
 ```
 The response follows the DecisionNode API:
@@ -167,11 +167,11 @@ The response follows the DecisionNode API:
 {
   "model": "decisionnode-edge-mini-0.9.1",
   "answers": {
-    "route": {"type": "choice", "choice": "billing", "confidence": 0.93, "probabilities": {"account": 0.02, "billing": 0.95, "bug": 0.03}},
-    "urgency": {"type": "score", "score": 2.2, "confidence": 0.57, "legend": {"0": "routine", "1": "today", "2": "urgent", "3": "critical"}, "probabilities": {"0": 0.01, "1": 0.09, "2": 0.59, "3": 0.31}},
-    "escalate": {"type": "truth", "truth": 0.74}
   },
-  "usage": {"input_tokens": 103, "output_tokens": 0}
 }
 ```

             "instructions": "How urgent is this?",
             "criteria": ["routine", "today", "urgent", "critical"],
         },
+        "refund": {"type": "truth", "instructions": "Refund this automatically?"},
     },
 })
+print(response["answers"]["route"]["choice"], response["answers"]["refund"]["truth"])
 ```
 The response follows the DecisionNode API:
 {
   "model": "decisionnode-edge-mini-0.9.1",
   "answers": {
+    "route": {"type": "choice", "choice": "billing", "confidence": 0.92, "probabilities": {"account": 0.02, "billing": 0.95, "bug": 0.03}},
+    "urgency": {"type": "score", "score": 1.75, "confidence": 0.67, "legend": {"0": "routine", "1": "today", "2": "urgent", "3": "critical"}, "probabilities": {"0": 0.01, "1": 0.27, "2": 0.68, "3": 0.04}},
+    "refund": {"type": "truth", "truth": 0.06}
   },
+  "usage": {"input_tokens": 100, "output_tokens": 0}
 }
 ```