File size: 5,423 Bytes
96e003f 0d50b9b 96e003f 0d50b9b 96e003f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 | import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from threading import Thread
import re
MODEL_PATH = "VDrontV2-0.1b-Title"
TEMPERATURE = 0.4
MAX_NEW_TOKENS = 64
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
STOP_TOKENS = ["<|endoftext|>", "(end_title)", "(title)", "\n\n\n"]
STOP_PATTERNS = [
r"\(end_title\)",
r"\(title\)",
r"<\|endoftext\|>",
]
def load_model_and_tokenizer(model_path):
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=False)
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16 if DEVICE == "cuda" else torch.float32,
device_map="auto",
trust_remote_code=False
)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
if tokenizer.eos_token is None:
tokenizer.eos_token = "<|endoftext|>"
tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids("<|endoftext|>")
return model, tokenizer
def should_stop(text, accumulated_text=""):
full_text = accumulated_text + text
for stop_token in STOP_TOKENS:
if stop_token in full_text:
return True, full_text.split(stop_token)[0]
if len(full_text) > 100:
last_part = full_text[-50:]
if len(set(last_part.split())) <= 3:
return True, full_text
if len(full_text) > 60:
for i in range(10, 30):
pattern = full_text[-i:]
if full_text.count(pattern) > 3:
return True, full_text
return False, full_text
def generate_stream(model, tokenizer, prompt, temperature=0.4, max_new_tokens=256):
inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=2048)
inputs = {k: v.to(model.device) for k, v in inputs.items()}
streamer = TextIteratorStreamer(
tokenizer,
skip_prompt=True,
skip_special_tokens=False,
timeout=30.0
)
generation_kwargs = dict(
**inputs,
max_new_tokens=max_new_tokens,
temperature=temperature,
do_sample=True,
top_p=0.96,
repetition_penalty=1.15,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id,
streamer=streamer,
)
thread = Thread(target=model.generate, kwargs=generation_kwargs)
thread.start()
accumulated_text = ""
try:
for new_text in streamer:
if new_text:
accumulated_text += new_text
stop, clean_text = should_stop(new_text,
accumulated_text[:-len(new_text)] if len(accumulated_text) > len(
new_text) else "")
if stop:
if clean_text and len(clean_text) > len(accumulated_text) - len(new_text):
yield clean_text
break
partial_stop = False
for stop_token in STOP_TOKENS:
for i in range(1, len(stop_token)):
if accumulated_text.endswith(stop_token[:i]):
partial_stop = True
break
if partial_stop:
break
if not partial_stop:
yield new_text
if len(accumulated_text) > 50 and len(accumulated_text.split()) > 10:
if any(pattern in accumulated_text for pattern in ["(end_title)", "(title)"]):
break
except Exception as e:
print(f"\n[Stream error: {e}]")
thread.join(timeout=5)
def text_continuation(model, tokenizer, temperature):
print(f"Instruct Mode (temp={temperature})")
print("Enter your text and the model will continue it.")
print("Commands: 'exit' or 'quit' — exit.")
print(f"Stop tokens: {', '.join(STOP_TOKENS)}")
print("=" * 60)
while True:
try:
user_input = input("\nUser: ").strip()
except (KeyboardInterrupt, EOFError):
print("\nGoodbye!")
break
if user_input.lower() in ["exit", "quit"]:
print("Goodbye!")
break
if not user_input:
continue
print("Title: ", end="", flush=True)
formatted_prompt = f"(user){user_input}(title)"
response_text = ""
for token in generate_stream(model, tokenizer, formatted_prompt, temperature=temperature,
max_new_tokens=MAX_NEW_TOKENS):
print(token, end="", flush=True)
response_text += token
if len(response_text.split()) > 50:
break
print()
if response_text:
print(f"[Generated {len(response_text.split())} words]")
if __name__ == "__main__":
print(f"Loading model from {MODEL_PATH}...")
model, tokenizer = load_model_and_tokenizer(MODEL_PATH)
print(f"Model loaded. Device: {DEVICE}")
print(f"Tokenizer eos_token: '{tokenizer.eos_token}'")
print(f"Tokenizer eos_token_id: {tokenizer.eos_token_id}")
text_continuation(model, tokenizer, temperature=TEMPERATURE) |