Instructions to use prithivMLmods/NuExtract3-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/NuExtract3-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="prithivMLmods/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("prithivMLmods/NuExtract3-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/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 prithivMLmods/NuExtract3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/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 prithivMLmods/NuExtract3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/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 prithivMLmods/NuExtract3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/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 prithivMLmods/NuExtract3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/NuExtract3-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/NuExtract3-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/NuExtract3-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/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": "prithivMLmods/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/prithivMLmods/NuExtract3-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/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 "prithivMLmods/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": "prithivMLmods/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 "prithivMLmods/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": "prithivMLmods/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 prithivMLmods/NuExtract3-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/NuExtract3-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use prithivMLmods/NuExtract3-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/NuExtract3-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/NuExtract3-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/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

Atomic Chat

NuExtract3-GGUF

NuExtract3 is NuMind's 4-billion-parameter unified vision-language model built on Qwen3.5-4B, designed for document understanding by combining strong structured information extraction with high-quality image-to-Markdown conversion, making it suitable for extraction pipelines, OCR, and RAG preprocessing across scans, receipts, forms, invoices, contracts, and tables. It accepts text, images, or both alongside a JSON template describing the desired output schema (with typed leaves like verbatim-string, date-time, currency, enums, and multi-enums), optional natural-language instructions, and optional in-context examples, and can also generate extraction templates from plain-language descriptions or convert documents directly into structured Markdown (with HTML tables and LaTeX math) — all while supporting both fast non-reasoning and deeper thinking inference modes. On NuMind's internal structured-extraction benchmark of ~600 diverse documents, NuExtract3.4-4B-RL achieves the top average score (0.651) with far fewer failed/non-deserializable outputs than larger models like Qwen3.5-9B, outperforming Gemma-4-E4B-it, GLM-4.6V-Flash, and Nemotron-3-Nano-Omni, with the paper noting that heavier reasoning actually hurts smaller models by triggering repetition loops and output-length failures. The model is deployable via vLLM with an OpenAI-compatible API supporting Multi-Token Prediction speculative decoding for faster throughput, or directly through Hugging Face Transformers, and is released under the Apache 2.0 license.

Model Files

File Name	Quant Type	File Size	File Link
NuExtract3.BF16.gguf	BF16	8.42 GB	Download
NuExtract3.Q3_K_L.gguf	Q3_K_L	2.42 GB	Download
NuExtract3.Q3_K_M.gguf	Q3_K_M	2.26 GB	Download
NuExtract3.Q3_K_S.gguf	Q3_K_S	2.07 GB	Download
NuExtract3.Q4_0.gguf	Q4_0	2.54 GB	Download
NuExtract3.Q4_K_M.gguf	Q4_K_M	2.71 GB	Download
NuExtract3.Q4_K_S.gguf	Q4_K_S	2.56 GB	Download
NuExtract3.Q5_0.gguf	Q5_0	2.99 GB	Download
NuExtract3.Q5_K_M.gguf	Q5_K_M	3.07 GB	Download
NuExtract3.Q5_K_S.gguf	Q5_K_S	2.99 GB	Download
NuExtract3.mmproj-bf16.gguf	mmproj-bf16	676 MB	Download