| # palmyra-vision | |
| ## usage | |
| ```py | |
| from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig | |
| from PIL import Image | |
| import requests | |
| import torch | |
| processor = AutoProcessor.from_pretrained( | |
| "Writer/palmyra-vision-dummy-weights", | |
| trust_remote_code=True, | |
| torch_dtype="auto", | |
| device_map="auto", | |
| use_fast=False, | |
| ) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| "Writer/palmyra-vision-dummy-weights", | |
| trust_remote_code=True, | |
| torch_dtype="auto", | |
| device_map="auto", | |
| ) | |
| inputs = processor.process( | |
| images=[ | |
| Image.open( | |
| requests.get("https://picsum.photos/seed/picsum/200/300", stream=True).raw | |
| ) | |
| ], | |
| text="what is this image about?", | |
| ) | |
| inputs = {k: v.to(model.device).unsqueeze(0) for k, v in inputs.items()} | |
| output = model.generate_from_batch( | |
| inputs, | |
| GenerationConfig(max_new_tokens=200, stop_strings="<|endoftext|>"), | |
| tokenizer=processor.tokenizer, | |
| ) | |
| generated_tokens = output[0, inputs["input_ids"].size(1) :] | |
| generated_text = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True) | |
| print(generated_text) | |
| ``` |