Instructions to use microsoft/Phi-3.5-vision-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use microsoft/Phi-3.5-vision-instruct with Transformers:

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

pipe = pipeline("image-text-to-text", model="microsoft/Phi-3.5-vision-instruct", 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 AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3.5-vision-instruct", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use microsoft/Phi-3.5-vision-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "microsoft/Phi-3.5-vision-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/Phi-3.5-vision-instruct",
		"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/microsoft/Phi-3.5-vision-instruct

SGLang

How to use microsoft/Phi-3.5-vision-instruct 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 "microsoft/Phi-3.5-vision-instruct" \
    --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": "microsoft/Phi-3.5-vision-instruct",
		"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 "microsoft/Phi-3.5-vision-instruct" \
        --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": "microsoft/Phi-3.5-vision-instruct",
		"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 microsoft/Phi-3.5-vision-instruct with Docker Model Runner:
```
docker model run hf.co/microsoft/Phi-3.5-vision-instruct
```

Fix spelling error in README

#10

by fromplutowithlove - opened Aug 23, 2024

base: refs/heads/main

←

from: refs/pr/10

Discussion Files changed

-2

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -59,7 +59,7 @@ Below are the comparison results on existing multi-image benchmarks. On average,
 **BLINK**: a benchmark with 14 visual tasks that humans can solve very quickly but are still hard for current multimodal LLMs.
-| Benchmark | Phi-3.5-vision-instrust | LlaVA-Interleave-Qwen-7B | InternVL-2-4B | InternVL-2-8B | Gemini-1.5-Flash | GPT-4o-mini | Claude-3.5-Sonnet | Gemini-1.5-Pro | GPT-4o |
 |--|--|--|--|--|--|--|--|--|--|
 | Art Style | 87.2 | 62.4 | 55.6 | 52.1 | 64.1 | 70.1 | 59.8 | 70.9 | 73.3 |
 | Counting | 54.2 | 56.7 | 54.2 | 66.7 | 51.7 | 55.0 | 59.2 | 65.0 | 65.0 |
@@ -79,7 +79,7 @@ Below are the comparison results on existing multi-image benchmarks. On average,
 **Video-MME**: comprehensively assess the capabilities of MLLMs in processing video data, covering a wide range of visual domains, temporal durations, and data modalities.
-| Benchmark | Phi-3.5-vision-instrust | LlaVA-Interleave-Qwen-7B | InternVL-2-4B | InternVL-2-8B | Gemini-1.5-Flash | GPT-4o-mini | Claude-3.5-Sonnet | Gemini-1.5-Pro | GPT-4o |
 |--|--|--|--|--|--|--|--|--|--|
 | short (<2min) | 60.8 | 62.3 | 60.7 | 61.7 | 72.2 | 70.1 | 66.3 | 73.3 | 77.7 |
 | medium (4-15min) | 47.7 | 47.1 | 46.4 | 49.6 | 62.7 | 59.6 | 54.7 | 61.2 | 68.0 |

 **BLINK**: a benchmark with 14 visual tasks that humans can solve very quickly but are still hard for current multimodal LLMs.
+| Benchmark | Phi-3.5-vision-instruct | LlaVA-Interleave-Qwen-7B | InternVL-2-4B | InternVL-2-8B | Gemini-1.5-Flash | GPT-4o-mini | Claude-3.5-Sonnet | Gemini-1.5-Pro | GPT-4o |
 |--|--|--|--|--|--|--|--|--|--|
 | Art Style | 87.2 | 62.4 | 55.6 | 52.1 | 64.1 | 70.1 | 59.8 | 70.9 | 73.3 |
 | Counting | 54.2 | 56.7 | 54.2 | 66.7 | 51.7 | 55.0 | 59.2 | 65.0 | 65.0 |
 **Video-MME**: comprehensively assess the capabilities of MLLMs in processing video data, covering a wide range of visual domains, temporal durations, and data modalities.
+| Benchmark | Phi-3.5-vision-instruct | LlaVA-Interleave-Qwen-7B | InternVL-2-4B | InternVL-2-8B | Gemini-1.5-Flash | GPT-4o-mini | Claude-3.5-Sonnet | Gemini-1.5-Pro | GPT-4o |
 |--|--|--|--|--|--|--|--|--|--|
 | short (<2min) | 60.8 | 62.3 | 60.7 | 61.7 | 72.2 | 70.1 | 66.3 | 73.3 | 77.7 |
 | medium (4-15min) | 47.7 | 47.1 | 46.4 | 49.6 | 62.7 | 59.6 | 54.7 | 61.2 | 68.0 |