File size: 1,334 Bytes
47bec6f d69bdb6 0ef4e30 c62b0e6 0ef4e30 d69bdb6 c62b0e6 d69bdb6 c62b0e6 d69bdb6 0ef4e30 c62b0e6 d69bdb6 0ef4e30 c62b0e6 d69bdb6 47bec6f |
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 |
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch
base_model = "Qwen/Qwen2.5-7B" # базовая модель
adapter_model = "muhtasham/tajik-qwen2.5-7b_finetuned" # таджикский fine-tune
tokenizer = AutoTokenizer.from_pretrained(base_model)
# Загружаем базовую модель
model = AutoModelForCausalLM.from_pretrained(
base_model,
torch_dtype="auto",
low_cpu_mem_usage=True,
device_map="cpu"
)
# Подключаем LoRA адаптер
model = PeftModel.from_pretrained(model, adapter_model)
def chat(prompt):
try:
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=150, temperature=0.8, top_p=0.9)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
except Exception as e:
return f"⚠️ Error: {str(e)}"
demo = gr.Interface(
fn=chat,
inputs=gr.Textbox(lines=3, label="Введите запрос (на таджикском, русском или английском)"),
outputs="text",
title="Tajik-Qwen2.5-7B Demo (LoRA)",
description="Файнтюнинг модели Qwen2.5-7B на таджикском языке."
)
if __name__ == "__main__":
demo.launch()
|