Instructions to use numind/NuExtract3-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use numind/NuExtract3-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="numind/NuExtract3-GGUF") 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 AutoModel model = AutoModel.from_pretrained("numind/NuExtract3-GGUF", dtype="auto") - llama-cpp-python
How to use numind/NuExtract3-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="numind/NuExtract3-GGUF", filename="NuExtract3-Q2_K.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use numind/NuExtract3-GGUF 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 numind/NuExtract3-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf numind/NuExtract3-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf numind/NuExtract3-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf numind/NuExtract3-GGUF: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 numind/NuExtract3-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf numind/NuExtract3-GGUF: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 numind/NuExtract3-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf numind/NuExtract3-GGUF:Q4_K_M
Use Docker
docker model run hf.co/numind/NuExtract3-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use numind/NuExtract3-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "numind/NuExtract3-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "numind/NuExtract3-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/numind/NuExtract3-GGUF:Q4_K_M
- SGLang
How to use numind/NuExtract3-GGUF 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 "numind/NuExtract3-GGUF" \ --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": "numind/NuExtract3-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "numind/NuExtract3-GGUF" \ --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": "numind/NuExtract3-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Ollama
How to use numind/NuExtract3-GGUF with Ollama:
ollama run hf.co/numind/NuExtract3-GGUF:Q4_K_M
- Unsloth Studio
How to use numind/NuExtract3-GGUF 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 numind/NuExtract3-GGUF 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 numind/NuExtract3-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for numind/NuExtract3-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use numind/NuExtract3-GGUF with Docker Model Runner:
docker model run hf.co/numind/NuExtract3-GGUF:Q4_K_M
- Lemonade
How to use numind/NuExtract3-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull numind/NuExtract3-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.NuExtract3-GGUF-Q4_K_M
List all available models
lemonade list
Would it be possible to enable MTP usage with the gguf?
Hey guys I'm very impressed with the results I'm getting with this model using the demo and I wanna try running locally using llama.cpp. As far as I understand it this model uses Qwen3.5-4b as a base which llama.cpp does support MTP usage for, so would it be possible to provide gguf files with mtp layers preserved to potentially get a nice little speed boost?