Instructions to use ig1/NuExtract-2.0-8B-FP8-Dynamic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ig1/NuExtract-2.0-8B-FP8-Dynamic with Transformers:

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

pipe = pipeline("image-text-to-text", model="ig1/NuExtract-2.0-8B-FP8-Dynamic")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("ig1/NuExtract-2.0-8B-FP8-Dynamic")
model = AutoModelForImageTextToText.from_pretrained("ig1/NuExtract-2.0-8B-FP8-Dynamic")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ig1/NuExtract-2.0-8B-FP8-Dynamic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ig1/NuExtract-2.0-8B-FP8-Dynamic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ig1/NuExtract-2.0-8B-FP8-Dynamic",
		"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/ig1/NuExtract-2.0-8B-FP8-Dynamic

SGLang

How to use ig1/NuExtract-2.0-8B-FP8-Dynamic 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 "ig1/NuExtract-2.0-8B-FP8-Dynamic" \
    --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": "ig1/NuExtract-2.0-8B-FP8-Dynamic",
		"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 "ig1/NuExtract-2.0-8B-FP8-Dynamic" \
        --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": "ig1/NuExtract-2.0-8B-FP8-Dynamic",
		"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"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use ig1/NuExtract-2.0-8B-FP8-Dynamic with Docker Model Runner:
```
docker model run hf.co/ig1/NuExtract-2.0-8B-FP8-Dynamic
```

NuExtract-2.0-8B-FP8-Dynamic

Quantization

Quantized with llm-compressor v0.9.0.1.

We used the original qwen2.5-vl example compression script and adapted it to a FP8-Dynamic compression recipe.

vLLM inference

docker run --rm --name 'NuExtract-2.0-8B' -e HF_TOKEN -v '/srv/cache:/root/.cache' -p 8000:8000 -e LD_LIBRARY_PATH='/lib/x86_64-linux-gnu:/usr/local/cuda/lib64' 'vllm/vllm-openai:v0.15.1-cu130' 'ig1/NuExtract-2.0-8B-FP8-Dynamic' --served-model-name 'NuExtract-2.0-8B' --trust-remote-code --limit-mm-per-prompt '{"image": 6, "video": 0}' --chat-template-content-format 'openai' --max-model-len 'auto' --kv-cache-memory-bytes '7G'

-e LD_LIBRARY_PATH=/lib64:/usr/local/cuda/lib64 is only needed if your host have a recent driver version (with native CUDA 13.0 or 13.1). See #32373 for more info.
Adapt /srv/cache to your liking, this will contains all cache data you want to keep for faster startup:
- dirs like huggingface, torch, vllm, flashinfer, etc...
To avoid eating up all the GPU VRAM, the --kv-cache-memory-bytes '7G' is set (it allows max model len by 1.02x)
- Feel free to adjust (or remove the flag and switch back to --gpu-memory-utilization 0.9) to increase or decrease KV cache to your liking

Check original project readme for openai like chat template use in requests.

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Safetensors

Model size

8B params

Tensor type

BF16

F8_E4M3

Model tree for ig1/NuExtract-2.0-8B-FP8-Dynamic

Base model

numind/NuExtract-2.0-8B

Quantized

(6)

this model