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
Download runtime/src/decisionnode_edge/__main__.py from bynn-intelligence-com/decisionnode-edge: direct link, hf CLI and curl.
- Browser
- Download file 1.98 kB
-
https://huggingface.co/bynn-intelligence-com/decisionnode-edge/resolve/main/runtime/src/decisionnode_edge/__main__.py
- Command line
-
hf download hf://bynn-intelligence-com/decisionnode-edge/runtime/src/decisionnode_edge/__main__.py
-
curl -L -o __main__.py https://huggingface.co/bynn-intelligence-com/decisionnode-edge/resolve/main/runtime/src/decisionnode_edge/__main__.py
1.98 kB
| """Answer one request from the command line, or serve the model over HTTP (server.py). | |
| python -m decisionnode_edge path/to/decisionnode-edge request.json # or "-" to read the request from stdin | |
| python -m decisionnode_edge serve path/to/decisionnode-edge --port 8000 | |
| """ | |
| from __future__ import annotations | |
| import argparse | |
| import json | |
| import sys | |
| from . import EdgeModel, RequestError | |
| def main(argv: list[str] | None = None) -> int: | |
| argv = sys.argv[1:] if argv is None else argv | |
| if argv[:1] == ["serve"]: | |
| from .server import main as serve | |
| return serve(argv[1:]) | |
| parser = argparse.ArgumentParser(prog="python -m decisionnode_edge", description="Answer a /v1/decide request with a local DecisionNode Edge model.") | |
| parser.add_argument("model", help="the model directory") | |
| parser.add_argument("request", help="a JSON file with the request body, or - for stdin") | |
| parser.add_argument("--device", default=None, help="cuda or cpu (default: cuda when present)") | |
| parser.add_argument("--dtype", default=None, help="bfloat16 or float32 (default: bfloat16 on cuda, float32 on cpu)") | |
| parser.add_argument("--backend", default=None, help="auto, torch, vllm, openvino, mlx or llamacpp (default: auto, the fastest installed)") | |
| parser.add_argument("--artifacts", default=None, help="the directory python -m decisionnode_edge.build wrote (openvino, llamacpp)") | |
| args = parser.parse_args(argv) | |
| body = json.load(sys.stdin if args.request == "-" else open(args.request, encoding="utf-8")) | |
| model = EdgeModel.load(args.model, device=args.device, dtype=args.dtype, backend=args.backend, artifacts=args.artifacts) | |
| try: | |
| response = model.decide(body) | |
| except RequestError as error: | |
| print(json.dumps({"error": str(error), "status": error.status}), file=sys.stderr) | |
| return 1 | |
| print(json.dumps(response, ensure_ascii=False, indent=2)) | |
| return 0 | |
| if __name__ == "__main__": | |
| sys.exit(main()) | |