Instructions to use nvidia/VideoITG-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nvidia/VideoITG-8B with Transformers:

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

pipe = pipeline("image-text-to-text", model="nvidia/VideoITG-8B")
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 EagleQwenG
model = EagleQwenG.from_pretrained("nvidia/VideoITG-8B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nvidia/VideoITG-8B with vLLM:

Install from pip and serve model

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

SGLang

How to use nvidia/VideoITG-8B 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 "nvidia/VideoITG-8B" \
    --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": "nvidia/VideoITG-8B",
		"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 "nvidia/VideoITG-8B" \
        --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": "nvidia/VideoITG-8B",
		"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 nvidia/VideoITG-8B with Docker Model Runner:
```
docker model run hf.co/nvidia/VideoITG-8B
```

VideoITG-8B / config.json

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	{
	"_name_or_path": "checkpoints-finetune/eagle-qwen2-7b-finetune-uni-ov-video-finetune-sftv1",
	"architectures": [
	"EagleQwenG"
	],
	"attention_dropout": 0.0,
	"bos_token_id": 151643,
	"eos_token_id": 151645,
	"freeze_mm_mlp_adapter": false,
	"hidden_act": "silu",
	"hidden_size": 3584,
	"image_aspect_ratio": "square",
	"initializer_range": 0.02,
	"intermediate_size": 18944,
	"max_position_embeddings": 32768,
	"max_window_layers": 28,
	"mm_hidden_size": 1152,
	"mm_patch_merge_type": "flat",
	"mm_projector_lr": null,
	"mm_projector_type": "seq_mlp",
	"mm_use_4_vision_tokens": false,
	"mm_use_im_patch_token": false,
	"mm_use_im_start_end": false,
	"mm_vision_select_feature": "patch",
	"mm_vision_select_layer": -2,
	"mm_vision_tower": "google/siglip-so400m-patch14-384",
	"model_type": "eagle_QwenG",
	"num_attention_heads": 28,
	"num_hidden_layers": 28,
	"num_key_value_heads": 4,
	"rms_norm_eps": 1e-06,
	"rope_scaling": null,
	"rope_theta": 1000000.0,
	"save_mm_mlp_adapter": false,
	"sliding_window": null,
	"tie_word_embeddings": false,
	"tokenizer_model_max_length": 16384,
	"tokenizer_padding_side": "right",
	"torch_dtype": "bfloat16",
	"transformers_version": "4.47.1",
	"tune_mm_mlp_adapter": false,
	"use_cache": true,
	"use_mm_proj": true,
	"use_sliding_window": false,
	"vision_min_num": 1,
	"vision_token_num": 8192,
	"vocab_size": 152064
	}