Evaluating Text-to-Visual Generation with Image-to-Text Generation
Paper • 2404.01291 • Published • 6
How to use zhiqiulin/clip-flant5-xl with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="zhiqiulin/clip-flant5-xl") # Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("zhiqiulin/clip-flant5-xl")
model = AutoModelForSeq2SeqLM.from_pretrained("zhiqiulin/clip-flant5-xl")How to use zhiqiulin/clip-flant5-xl with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zhiqiulin/clip-flant5-xl"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "zhiqiulin/clip-flant5-xl",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/zhiqiulin/clip-flant5-xl
How to use zhiqiulin/clip-flant5-xl with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "zhiqiulin/clip-flant5-xl" \
--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": "zhiqiulin/clip-flant5-xl",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "zhiqiulin/clip-flant5-xl" \
--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": "zhiqiulin/clip-flant5-xl",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use zhiqiulin/clip-flant5-xl with Docker Model Runner:
docker model run hf.co/zhiqiulin/clip-flant5-xl
This model is a fine-tuned version of google/flan-t5-xl designed for image-text retrieval tasks, as presented in the VQAScore paper.
Base model
google/flan-t5-xl