Instructions to use BoyangZ/Phi3-VLM-chinese-english-llava-1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BoyangZ/Phi3-VLM-chinese-english-llava-1.5 with Transformers:

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

pipe = pipeline("text-generation", model="BoyangZ/Phi3-VLM-chinese-english-llava-1.5", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("BoyangZ/Phi3-VLM-chinese-english-llava-1.5", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BoyangZ/Phi3-VLM-chinese-english-llava-1.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BoyangZ/Phi3-VLM-chinese-english-llava-1.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BoyangZ/Phi3-VLM-chinese-english-llava-1.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BoyangZ/Phi3-VLM-chinese-english-llava-1.5

SGLang

How to use BoyangZ/Phi3-VLM-chinese-english-llava-1.5 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 "BoyangZ/Phi3-VLM-chinese-english-llava-1.5" \
    --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": "BoyangZ/Phi3-VLM-chinese-english-llava-1.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "BoyangZ/Phi3-VLM-chinese-english-llava-1.5" \
        --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": "BoyangZ/Phi3-VLM-chinese-english-llava-1.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BoyangZ/Phi3-VLM-chinese-english-llava-1.5 with Docker Model Runner:
```
docker model run hf.co/BoyangZ/Phi3-VLM-chinese-english-llava-1.5
```

Phi3-VLM-chinese-english-llava-1.5 / runs /May06_08-51-43_A100 /events.out.tfevents.1714956739.A100.27194.0

BoyangZ

Upload folder using huggingface_hub

1b5e739 verified about 2 years ago

download

history blame contribute delete

9.13 kB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 478366c993aaf90d42267bb9bbb949766bd7ff940f1bccece6b8dbe05b8858b9
Size of remote file:: 9.13 kB
SHA256:: 191438ece0bbeae6b1a5ea7f690031adc5b17bf7e6545659b33b04fb44e50185

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.