Image-Text-to-Text
Transformers
Safetensors
GGUF
qwen3_5
system-one
decision-making
classification
calibration
structured-output
on-device
multimodal
Instructions to use bynn-intelligence-com/decisionnode-edge-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Commit History
2 days ago
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Runtime 0.9.2.1: the MLX backend bounds the memory it keeps between requests
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5 days ago
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DecisionNode Edge Mini 0.9.2: point and box questions, the /v1/systemone route as hosted, a box calibration
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Model card: DecisionNode Flash for speed, DecisionNode for the highest accuracy
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Model card: the quick-start answer from the published weights
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Model card: point and box questions on the hosted API
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Model card: the hosted models for the highest accuracy
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Model card: System One, recommended setup
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Model card: DecisionNode logotype
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DecisionNode Edge Mini 0.9.1
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initial commit
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