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 config.json from bynn-intelligence-com/decisionnode-edge: direct link, hf CLI and curl.
- Browser
- Download file 2.71 kB
-
https://huggingface.co/bynn-intelligence-com/decisionnode-edge/resolve/main/config.json
- Command line
-
hf download hf://bynn-intelligence-com/decisionnode-edge/config.json
-
curl -L -o config.json https://huggingface.co/bynn-intelligence-com/decisionnode-edge/resolve/main/config.json
2.71 kB
| { | |
| "architectures": [ | |
| "Qwen3_5ForConditionalGeneration" | |
| ], | |
| "dtype": "bfloat16", | |
| "image_token_id": 248056, | |
| "model_type": "qwen3_5", | |
| "text_config": { | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "attn_output_gate": true, | |
| "bos_token_id": null, | |
| "dtype": "bfloat16", | |
| "eos_token_id": 248044, | |
| "full_attention_interval": 4, | |
| "head_dim": 256, | |
| "hidden_act": "silu", | |
| "hidden_size": 2048, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 6144, | |
| "layer_types": [ | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention" | |
| ], | |
| "linear_conv_kernel_dim": 4, | |
| "linear_key_head_dim": 128, | |
| "linear_num_key_heads": 16, | |
| "linear_num_value_heads": 16, | |
| "linear_value_head_dim": 128, | |
| "mamba_ssm_dtype": "float32", | |
| "max_position_embeddings": 262144, | |
| "mlp_only_layers": [], | |
| "model_type": "qwen3_5_text", | |
| "mtp_num_hidden_layers": 0, | |
| "mtp_use_dedicated_embeddings": false, | |
| "num_attention_heads": 8, | |
| "num_hidden_layers": 24, | |
| "num_key_value_heads": 2, | |
| "pad_token_id": null, | |
| "partial_rotary_factor": 0.25, | |
| "rms_norm_eps": 1e-06, | |
| "rope_parameters": { | |
| "mrope_interleaved": true, | |
| "mrope_section": [ | |
| 11, | |
| 11, | |
| 10 | |
| ], | |
| "partial_rotary_factor": 0.25, | |
| "rope_theta": 10000000, | |
| "rope_type": "default" | |
| }, | |
| "tie_word_embeddings": true, | |
| "use_cache": true, | |
| "vocab_size": 248384 | |
| }, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.18.0", | |
| "video_token_id": 248057, | |
| "vision_config": { | |
| "deepstack_visual_indexes": [], | |
| "depth": 24, | |
| "dtype": "bfloat16", | |
| "hidden_act": "gelu_pytorch_tanh", | |
| "hidden_size": 1024, | |
| "in_channels": 3, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "model_type": "qwen3_5_vision", | |
| "num_heads": 16, | |
| "num_position_embeddings": 2304, | |
| "out_hidden_size": 2048, | |
| "patch_size": 16, | |
| "rope_parameters": { | |
| "rope_theta": 10000.0, | |
| "rope_type": "axial" | |
| }, | |
| "spatial_merge_size": 2, | |
| "temporal_patch_size": 2 | |
| }, | |
| "vision_end_token_id": 248054, | |
| "vision_start_token_id": 248053 | |
| } | |