Transformers How to use nlpai-lab/RenderRank-2B with Transformers:
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM
processor = AutoProcessor.from_pretrained("nlpai-lab/RenderRank-2B", trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained("nlpai-lab/RenderRank-2B", trust_remote_code=True, device_map="auto")
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?"}
]
},
]
inputs = processor.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) sentence-transformers How to use nlpai-lab/RenderRank-2B with sentence-transformers:
from sentence_transformers import CrossEncoder
model = CrossEncoder("nlpai-lab/RenderRank-2B", trust_remote_code=True)
query = "Which planet is known as the Red Planet?"
passages = [
"Venus is often called Earth's twin because of its similar size and proximity.",
"Mars, known for its reddish appearance, is often referred to as the Red Planet.",
"Jupiter, the largest planet in our solar system, has a prominent red spot.",
"Saturn, famous for its rings, is sometimes mistaken for the Red Planet."
]
scores = model.predict([(query, passage) for passage in passages])
print(scores) 
