Instructions to use FierceLLM/CoreGPT-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use FierceLLM/CoreGPT-small with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("sberbank-ai/rugpt3small_based_on_gpt2") model = PeftModel.from_pretrained(base_model, "FierceLLM/CoreGPT-small") - Notebooks
- Google Colab
- Kaggle
Delete inference.py
Browse files- inference.py +0 -33
inference.py
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from peft import PeftModel
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# 1. Пути к моделям
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base_model_id = "sberbank-ai/rugpt3small_based_on_gpt2"
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# Укажите путь к папке, которую вы скачали из Colab (например, checkpoint-1860)
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adapter_path = "."
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# 2. Загрузка токенизатора и базовой модели
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tokenizer = AutoTokenizer.from_pretrained(base_model_id)
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model = AutoModelForCausalLM.from_pretrained(base_model_id, torch_dtype=torch.float16, device_map="auto")
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# 3. Подгрузка LoRA адаптера
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model = PeftModel.from_pretrained(model, adapter_path)
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model.eval()
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def generate(text):
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prompt = f"Пользователь: {text}\nПомощник:"
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=150,
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do_sample=True,
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top_p=0.9,
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repetition_penalty=1.2
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)
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return tokenizer.decode(outputs[0], skip_special_tokens=True).split("Помощник:")[-1].strip()
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# Тест
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print(generate(input('Prompt: ')))
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