Spaces:
Running on Zero
Running on Zero
| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig | |
| import torch | |
| import spaces # <-- правильный импорт (пакет spaces) | |
| MODEL = "covaga/eplan-assistant-v3-merged" | |
| print("Loading model...") | |
| tokenizer = AutoTokenizer.from_pretrained(MODEL) | |
| quant_config = BitsAndBytesConfig( | |
| load_in_4bit=True, | |
| bnb_4bit_compute_dtype=torch.float16, | |
| ) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| MODEL, | |
| device_map="auto", | |
| quantization_config=quant_config, | |
| ) | |
| print("Model loaded.") | |
| # <-- используем декоратор из пакета spaces | |
| def respond(message, history): | |
| messages = [{"role": "user", "content": message}] | |
| inputs = tokenizer.apply_chat_template( | |
| messages, | |
| add_generation_prompt=True, | |
| tokenize=True, | |
| return_dict=True, | |
| return_tensors="pt" | |
| ).to(model.device) | |
| outputs = model.generate( | |
| **inputs, | |
| max_new_tokens=512, | |
| do_sample=True, | |
| temperature=0.7 | |
| ) | |
| answer = tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True) | |
| return answer | |
| demo = gr.ChatInterface( | |
| fn=respond, | |
| title="EPLAN Assistant", | |
| description="Вопросы по EPLAN Electric 2026" | |
| ) | |
| demo.launch() |