rushai-dev/name_gen
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How to use rushai-dev/THAI-TrOCR with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-text-to-text", model="rushai-dev/THAI-TrOCR") # Load model directly
from transformers import AutoTokenizer, AutoModelForImageTextToText
tokenizer = AutoTokenizer.from_pretrained("rushai-dev/THAI-TrOCR")
model = AutoModelForImageTextToText.from_pretrained("rushai-dev/THAI-TrOCR")How to use rushai-dev/THAI-TrOCR with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rushai-dev/THAI-TrOCR"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "rushai-dev/THAI-TrOCR",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/rushai-dev/THAI-TrOCR
How to use rushai-dev/THAI-TrOCR with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "rushai-dev/THAI-TrOCR" \
--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": "rushai-dev/THAI-TrOCR",
"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 "rushai-dev/THAI-TrOCR" \
--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": "rushai-dev/THAI-TrOCR",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use rushai-dev/THAI-TrOCR with Docker Model Runner:
docker model run hf.co/rushai-dev/THAI-TrOCR
from transformers import TrOCRProcessor, AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel
encode = 'rushai-dev/THAI-TrOCR'
decode = "xlm-roberta-base"
tokenizer = AutoTokenizer.from_pretrained(decode)
feature_extractor = ViTFeatureExtractor.from_pretrained(encode)
processor = TrOCRProcessor(feature_extractor=feature_extractor, tokenizer=tokenizer)
model = VisionEncoderDecoderModel.from_pretrained(encode)
from PIL import Image
image = Image.open("xxxxxxx.png").convert("RGB")
image
pixel_values = processor(image, return_tensors="pt").pixel_values
generated_ids = model.generate(pixel_values)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
generated_text