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from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForSeq2SeqLM, TextStreamer
import transformers
import torch
from huggingface_hub import login
import os
import logging
login(token = os.getenv('HF_TOKEN'))
class Model(torch.nn.Module):
number_of_models = 0
__model_list__ = [
"Qwen/Qwen2-1.5B-Instruct",
"lmsys/vicuna-7b-v1.5",
"google-t5/t5-large",
"mistralai/Mistral-7B-Instruct-v0.1",
"meta-llama/Meta-Llama-3.1-8B-Instruct"
]
def __init__(self, model_name="Qwen/Qwen2-1.5B-Instruct") -> None:
super(Model, self).__init__()
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.name = model_name
logging.info(f'start loading model {self.name}')
if model_name == "google-t5/t5-large":
# For T5 or any other Seq2Seq model
self.model = AutoModelForSeq2SeqLM.from_pretrained(
model_name, torch_dtype=torch.bfloat16, device_map="auto"
)
else:
# For GPT-like models or other causal language models
self.model = AutoModelForCausalLM.from_pretrained(
model_name, torch_dtype=torch.bfloat16, device_map="auto"
)
logging.info(f'Loaded model {self.name}')
self.update()
@classmethod
def update(cls):
cls.number_of_models += 1
def return_mode_name(self):
return self.name
def return_tokenizer(self):
return self.tokenizer
def return_model(self):
return self.pipeline
def gen(self, content_list, temp=0.001, max_length=500, streaming=False):
# Convert list of texts to input IDs
input_ids = self.tokenizer(content_list, return_tensors="pt", padding=True, truncation=True).input_ids.to(self.model.device)
if streaming:
# Process each input separately
for single_input_ids in input_ids:
# Set up the initial generation parameters
gen_kwargs = {
"input_ids": single_input_ids.unsqueeze(0),
"max_new_tokens": max_length,
"do_sample": True,
"temperature": temp,
"eos_token_id": self.tokenizer.eos_token_id,
}
# Generate and yield tokens one by one
unfinished_sequences = single_input_ids.unsqueeze(0)
while unfinished_sequences.shape[1] < gen_kwargs["max_new_tokens"]:
with torch.no_grad():
output = self.model.generate(**gen_kwargs, max_new_tokens=1, return_dict_in_generate=True, output_scores=True)
next_token_logits = output.scores[0][0]
next_token = torch.argmax(next_token_logits, dim=-1).unsqueeze(0)
unfinished_sequences = torch.cat([unfinished_sequences, next_token], dim=-1)
# Yield the newly generated token
yield self.tokenizer.decode(next_token[0], skip_special_tokens=True)
if next_token.item() == self.tokenizer.eos_token_id:
break
# Update input_ids for the next iteration
gen_kwargs["input_ids"] = unfinished_sequences
else:
# Non-streaming generation (unchanged)
outputs = self.model.generate(
input_ids,
max_new_tokens=max_length,
do_sample=True,
temperature=temp,
eos_token_id=self.tokenizer.eos_token_id,
)
return self.tokenizer.batch_decode(outputs, skip_special_tokens=True)
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