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Update app.py
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app.py
CHANGED
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@@ -2,36 +2,46 @@ import gradio as gr
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
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from threading import Thread
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MODEL_ID = "."
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_ID,
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torch_dtype=torch.
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low_cpu_mem_usage=True,
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device_map="
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)
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def predict(message, history):
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#
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for msg in history:
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# Просто переносим роли, которые понимает модель (User/Bot)
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role = "User" if msg["role"] == "user" else "Bot"
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prompt += f"{role}: {msg['content']}\n"
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#
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prompt += f"User: {message}\nBot:"
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inputs = tokenizer(prompt, return_tensors="pt")
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streamer = TextIteratorStreamer(tokenizer, timeout=
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generate_kwargs = dict(
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**inputs,
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streamer=streamer,
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max_new_tokens=
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do_sample=True,
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temperature=0.7,
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repetition_penalty=1.2,
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@@ -43,13 +53,18 @@ def predict(message, history):
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partial_message = ""
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for new_token in streamer:
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# Если модель в порыве шизы начнет писать за "User:", обрезаем
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if "User:" in new_token:
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break
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partial_message += new_token
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yield partial_message
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if __name__ == "__main__":
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
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from threading import Thread
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import os
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# Папка для экстренного сброса весов, если RAM все равно будет не хватать
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os.makedirs("offload", exist_ok=True)
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MODEL_ID = "."
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print("🍌 BananaGPT: Загрузка в float16...")
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# Загружаем токенизатор
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
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# Загружаем модель: float16 режет потребление памяти в 2 раза
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_ID,
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torch_dtype=torch.float16,
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low_cpu_mem_usage=True,
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device_map="auto",
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offload_folder="offload"
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)
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def predict(message, history):
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# ПУСТОЙ промпт (никаких системных инструкций, как ты и просил)
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prompt = ""
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# СТРУКТУРА: переносим историю диалога
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for msg in history:
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role = "User" if msg["role"] == "user" else "Bot"
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prompt += f"{role}: {msg['content']}\n"
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# ЗАПРОС: добавляем текущее сообщение
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prompt += f"User: {message}\nBot:"
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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streamer = TextIteratorStreamer(tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True)
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generate_kwargs = dict(
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**inputs,
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streamer=streamer,
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max_new_tokens=128,
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do_sample=True,
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temperature=0.7,
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repetition_penalty=1.2,
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partial_message = ""
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for new_token in streamer:
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if "User:" in new_token:
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break
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partial_message += new_token
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yield partial_message
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# Интерфейс
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demo = gr.ChatInterface(
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predict,
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type="messages",
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title="BananaGPT (float16)"
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)
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if __name__ == "__main__":
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# Запуск на порту 7860 для HF Spaces
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demo.queue().launch(server_name="0.0.0.0", server_port=7860)
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