| from PIL import Image |
| import requests |
|
|
| from .base import BaseModel |
|
|
|
|
| class Falcon2VLM(BaseModel): |
|
|
| INSTALL_REQ = False |
| INTERLEAVE = False |
|
|
| def __init__(self, model_path='tiiuae/falcon-11B-vlm', **kwargs): |
| import torch |
| from transformers import LlavaNextForConditionalGeneration, LlavaNextProcessor |
|
|
| self.model_path = model_path |
| self.processor = LlavaNextProcessor.from_pretrained(model_path, tokenizer_class='PreTrainedTokenizerFast') |
| self.model = LlavaNextForConditionalGeneration.from_pretrained( |
| model_path, torch_dtype=torch.bfloat16, device_map='cuda').eval() |
| default_kwargs = {'max_new_tokens': 512} |
| default_kwargs.update(kwargs) |
| self.kwargs = default_kwargs |
|
|
| def generate_inner(self, message, dataset=None): |
| prompt, image_path = self.message_to_promptimg(message, dataset=dataset) |
| image = Image.open(image_path).convert('RGB') |
|
|
| prompt = f'User:<image>\n{prompt} Falcon:' |
| inputs = self.processor(text=prompt, images=image, return_tensors='pt').to('cuda') |
|
|
| output = self.model.generate(**inputs, **self.kwargs) |
| prompt_length = inputs['input_ids'].shape[1] |
| model_response = self.processor.decode(output[0][prompt_length:], skip_special_tokens=True).strip() |
| return model_response |
|
|
