- Libraries
- Transformers.js
How to use Xenova/tiny-random-LlavaForConditionalGeneration_phi with Transformers.js:
// npm i @huggingface/transformers
import { pipeline } from '@huggingface/transformers';
// Allocate pipeline
const pipe = await pipeline('image-text-to-text', 'Xenova/tiny-random-LlavaForConditionalGeneration_phi'); - Transformers
How to use Xenova/tiny-random-LlavaForConditionalGeneration_phi with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-text-to-text", model="Xenova/tiny-random-LlavaForConditionalGeneration_phi") # Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText
processor = AutoProcessor.from_pretrained("Xenova/tiny-random-LlavaForConditionalGeneration_phi")
model = AutoModelForImageTextToText.from_pretrained("Xenova/tiny-random-LlavaForConditionalGeneration_phi") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Xenova/tiny-random-LlavaForConditionalGeneration_phi with vLLM:
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Xenova/tiny-random-LlavaForConditionalGeneration_phi"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Xenova/tiny-random-LlavaForConditionalGeneration_phi",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Use Docker
docker model run hf.co/Xenova/tiny-random-LlavaForConditionalGeneration_phi
- SGLang
How to use Xenova/tiny-random-LlavaForConditionalGeneration_phi 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 "Xenova/tiny-random-LlavaForConditionalGeneration_phi" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Xenova/tiny-random-LlavaForConditionalGeneration_phi",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "Xenova/tiny-random-LlavaForConditionalGeneration_phi" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Xenova/tiny-random-LlavaForConditionalGeneration_phi",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}' - Docker Model Runner
How to use Xenova/tiny-random-LlavaForConditionalGeneration_phi with Docker Model Runner:
docker model run hf.co/Xenova/tiny-random-LlavaForConditionalGeneration_phi
Install from pip and serve model
# Install vLLM from pip: pip install vllm# Start the vLLM server: vllm serve "Xenova/tiny-random-LlavaForConditionalGeneration_phi"# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Xenova/tiny-random-LlavaForConditionalGeneration_phi", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'