Text Generation
Transformers
Safetensors
GGUF
qwen3_5_moe
image-text-to-text
flywheel
opspot
vertical-ai-employee
construction
conversational
Instructions to use flywheel-ai/construction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flywheel-ai/construction with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="flywheel-ai/construction") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("flywheel-ai/construction") model = AutoModelForMultimodalLM.from_pretrained("flywheel-ai/construction") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - llama-cpp-python
How to use flywheel-ai/construction with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="flywheel-ai/construction", filename="model-q4_k_m.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use flywheel-ai/construction 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 flywheel-ai/construction:Q4_K_M # Run inference directly in the terminal: llama cli -hf flywheel-ai/construction:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf flywheel-ai/construction:Q4_K_M # Run inference directly in the terminal: llama cli -hf flywheel-ai/construction:Q4_K_M
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 flywheel-ai/construction:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf flywheel-ai/construction:Q4_K_M
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 flywheel-ai/construction:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf flywheel-ai/construction:Q4_K_M
Use Docker
docker model run hf.co/flywheel-ai/construction:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use flywheel-ai/construction with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "flywheel-ai/construction" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "flywheel-ai/construction", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/flywheel-ai/construction:Q4_K_M
- SGLang
How to use flywheel-ai/construction 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 "flywheel-ai/construction" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "flywheel-ai/construction", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "flywheel-ai/construction" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "flywheel-ai/construction", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use flywheel-ai/construction with Ollama:
ollama run hf.co/flywheel-ai/construction:Q4_K_M
- Unsloth Studio
How to use flywheel-ai/construction with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for flywheel-ai/construction to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for flywheel-ai/construction to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for flywheel-ai/construction to start chatting
- Pi
How to use flywheel-ai/construction with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf flywheel-ai/construction:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "flywheel-ai/construction:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use flywheel-ai/construction with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf flywheel-ai/construction:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default flywheel-ai/construction:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use flywheel-ai/construction with Docker Model Runner:
docker model run hf.co/flywheel-ai/construction:Q4_K_M
- Lemonade
How to use flywheel-ai/construction with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull flywheel-ai/construction:Q4_K_M
Run and chat with the model
lemonade run user.construction-Q4_K_M
List all available models
lemonade list
Ctrl+K