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 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
DecisionNode Edge 0.9.2: point and box questions, the /v1/systemone route as hosted, a box calibration
7706b50 verified Download decisionnode_edge.json from bynn-intelligence-com/decisionnode-edge: direct link, hf CLI and curl.
- Browser
- Download file 6.26 kB
-
https://huggingface.co/bynn-intelligence-com/decisionnode-edge/resolve/main/decisionnode_edge.json
- Command line
-
hf download hf://bynn-intelligence-com/decisionnode-edge/decisionnode_edge.json
-
curl -L -o decisionnode_edge.json https://huggingface.co/bynn-intelligence-com/decisionnode-edge/resolve/main/decisionnode_edge.json
6.26 kB
| { | |
| "format": "decisionnode-edge-1", | |
| "name": "decisionnode-edge-0.9.1", | |
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| "image_labels": "Picture <n> (<id>): ", | |
| "answer_cue": "Answer", | |
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| "first_token_id": 248077, | |
| "code_seed_hex": "6a6566662d74656d706c6174652d32" | |
| }, | |
| "readout": { | |
| "file": "readout.safetensors", | |
| "tensor": "readout.weight", | |
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| "rows": 261, | |
| "dtype": "float32" | |
| }, | |
| "calibration": "calibration.json", | |
| "limits": { | |
| "max_input_tokens": 16384, | |
| "max_image_side": 2048, | |
| "max_images": 16, | |
| "max_options": 255, | |
| "max_levels": 10, | |
| "max_grid_values": 4096 | |
| }, | |
| "box_calibration_sha256": "544b3d40cc9e355b16f8c131426c21245c1b5847a46c40c1b4be2c33a447b4fa" | |
| } | |