Instructions to use nvidia/NVLM-D-72B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nvidia/NVLM-D-72B with Transformers:

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

pipe = pipeline("image-text-to-text", model="nvidia/NVLM-D-72B", trust_remote_code=True)
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 NVLM_D
model = NVLM_D.from_pretrained("nvidia/NVLM-D-72B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nvidia/NVLM-D-72B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nvidia/NVLM-D-72B"
# 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/NVLM-D-72B",
		"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/NVLM-D-72B

SGLang

How to use nvidia/NVLM-D-72B 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/NVLM-D-72B" \
    --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/NVLM-D-72B",
		"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/NVLM-D-72B" \
        --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/NVLM-D-72B",
		"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/NVLM-D-72B with Docker Model Runner:
```
docker model run hf.co/nvidia/NVLM-D-72B
```

boxinw@nvidia.com commited on Oct 2, 2024

Commit

732e074

1 Parent(s): 9cf8c9e

Add text only results

Browse files

Files changed (1) hide show

README.md +24 -0

README.md CHANGED Viewed

@@ -34,6 +34,8 @@ We provide the results from both the Huggingface codebase and the Megatron codeb
 Results (as of September 17th, 2024) in the multimodal benchmarks are as follows:
 | Benchmark                    | MMMU (val / test) | MathVista | OCRBench | AI2D | ChartQA | DocVQA | TextVQA | RealWorldQA | VQAv2 |
 |------------------------------|-------------------|-----------|----------|------|---------|--------|---------|-------------|-------|
 | NVLM-D 1.0 72B (Huggingface) | 58.7 / 54.9       | 65.2      | 852      | 94.2 | 86.0    | 92.6   | 82.6    | 69.5        | 85.4  |
@@ -47,6 +49,28 @@ Results (as of September 17th, 2024) in the multimodal benchmarks are as follows
 | Claude 3.5 Sonnet            | 68.3 / -          | 67.7      | 788      | 94.7 | 90.8    | 95.2   | -       | -           | -     |
 | Gemini 1.5 Pro (Aug 2024)    | 62.2 / -          | 63.9      | 754      | 94.4 | 87.2    | 93.1   | 78.7    | 70.4        | 80.2  |
 ## How to use

 Results (as of September 17th, 2024) in the multimodal benchmarks are as follows:
+### Vision-language Benchmarks
 | Benchmark                    | MMMU (val / test) | MathVista | OCRBench | AI2D | ChartQA | DocVQA | TextVQA | RealWorldQA | VQAv2 |
 |------------------------------|-------------------|-----------|----------|------|---------|--------|---------|-------------|-------|
 | NVLM-D 1.0 72B (Huggingface) | 58.7 / 54.9       | 65.2      | 852      | 94.2 | 86.0    | 92.6   | 82.6    | 69.5        | 85.4  |
 | Claude 3.5 Sonnet            | 68.3 / -          | 67.7      | 788      | 94.7 | 90.8    | 95.2   | -       | -           | -     |
 | Gemini 1.5 Pro (Aug 2024)    | 62.2 / -          | 63.9      | 754      | 94.4 | 87.2    | 93.1   | 78.7    | 70.4        | 80.2  |
+### Text-only Benchmarks
+| Tasks                        | Backbone LLM | MMLU | GSM8K | MATH | HumanEval | Avg. Accuracy    |
+|------------------------------|--------------|------|-------|------|-----------|------------------|
+| **Proprietary**              |              |      |       |      |           |                  |
+| GPT-4.0                      | N/A          | 88.7 | -     | 76.6 | 90.2      | -                |
+| Gemini Pro 1.5 (Aug 2024)    | N/A          | 85.9 | 90.8  | 67.7 | 84.1      | 82.1             |
+| Claude 3.5 Sonnet            | N/A          | 88.7 | 96.4  | 71.1 | 92.0      | 87.0             |
+| **Open LLM**                 |              |      |       |      |           |                  |
+| (a) Nous-Hermes-2-Yi-34B     | N/A          | 75.5 | 78.6  | 21.8 | 43.3      | 54.8             |
+| (b) Qwen-72B-Instruct        | N/A          | 82.3 | 91.1  | 59.7 | 86.0      | 79.8             |
+| (c) Llama-3-70B-Instruct     | N/A          | 82.0 | 93.0  | 51.0 | 81.7      | 76.6             |
+| (d) Llama-3.1-70B-Instruct   | N/A          | 83.6 | 95.1  | 68.0 | 80.5      | 81.8             |
+| (e) Llama-3.1-405B-Instruct  | N/A          | 87.3 | 96.8  | 73.8 | 89.0      | 86.7             |
+| **Open Multimodal LLM**      |              |      |       |      |           |                  |
+| VILA-1.5 40B                 | (a)          | 73.3 | 67.5  | 16.8 | 34.1      | 🥶 47.9   (-6.9) |
+| LLaVA-OneVision 72B          | (b)          | 80.6 | 89.9  | 49.2 | 74.4      | 🥶 73.5   (-6.3) |
+| InternVL-2-Llama3-76B        | (c)          | 78.5 | 87.1  | 42.5 | 71.3      | 🥶 69.9   (-6.7) |
+| *Llama 3-V 70B               | (d)          | 83.6 | 95.1  | 68.0 | 80.5      | 🙂 81.8   (0)    |
+| *Llama 3-V 405B              | (e)          | 87.3 | 96.8  | 73.8 | 89.0      | 🙂 86.7   (0)    |
+| NVLM-D 1.0 72B (Megatron)    | (b)          | 82.0 | 92.9  | 73.1 | 88.4      | 🥳 84.1   (+4.3) |
+| NVLM-D 1.0 72B (Huggingface) | (b)          | 81.7 | 93.2  | 73.1 | 89.0      | 🥳 84.3   (+4.5) |
 ## How to use