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import torch
from transformers import AutoProcessor, AutoModel, TextIteratorStreamer
class FullSequenceStreamer(TextIteratorStreamer):
def __init__(self, tokenizer, **kwargs):
super().__init__(tokenizer, **kwargs)
self.mask_token = tokenizer.mask_token_id
self.placeholder_token = tokenizer.convert_tokens_to_ids("_")
self.placeholder_token = tokenizer.encode("␣")[0]
def put(self, value, stream_end=False):
# change mask tokens to space token
value = value.clone()
value[value == self.mask_token] = self.placeholder_token
# Assume full token_ids are passed in every time
decoded = self.tokenizer.batch_decode(value, **self.decode_kwargs)
self.text_queue.put(decoded)
if stream_end:
self.text_queue.put(self.stop_signal, timeout=self.timeout)
def end(self):
self.text_queue.put(self.stop_signal, timeout=self.timeout)
def get_model(device):
model_name = "rp-yu/Dimple-7B"
processor = AutoProcessor.from_pretrained(
model_name,
trust_remote_code=True
)
model = AutoModel.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
)
model = model.eval()
model = model.to(device)
return model, processor
from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
def get_qwen(device):
model_name = "Qwen/Qwen2-VL-7B-Instruct"
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
model = Qwen2VLForConditionalGeneration.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
)
model = model.eval()
model = model.to(device)
return model, processor |