vi/3_parameter_efficient_finetuning/notebooks/load_lora

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}, "source": [ "## Sử dụng adapters từ Hugging Face Hub" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 517, "referenced_widgets": [ "ff2454cf69b346fea70070522cf93689", "87b36eb4ad3b4047a32c7d67a5aabc5e", "8bf9fd4bd28e4bc1b5895bc9315e727e", "01845549768a4db580b5555809e83342", "a490025901df478f93be1f19c1be4b09", "4f0c97b8555942aea6a8003228c07427", "80aea5c9422648ef91292d75fb46c8dc", "8af46fc543bf46a68ac78c2e8e7e9ddc", "b7b8897c988445ae8f9db9b928674477", "4bc518f16b7644e5a11bda66cc285d89", "441a953149814848a24edc9398a2ef63", "75913676a5df43fbbfe744b8882188df", "1e94c570880449d0a2763bc1eba09057", "d72bd17e161442b0979dceaaef66d82c", "2396a162f70d41a384afd1fcb2f78d11", "39f1a52884e3440c8b521e493d6e1a53", "528b93d088054955906282b1c9f93a17", "99b9d36317a34143acf982fa3b089fda", "4f592d7632fd440aac0bf97ceed2de75", "1b515483e5884479b2101127c16321d4", "ce8d4bac782949579a2e52864455d9de", "523deba849044669bfe8c959750708fc" ] }, "id": "hsD1VKqeA62Z", "outputId": "83ecbf31-3f84-4b8e-d667-10317f7adcec" }, "outputs": [], "source": [ "import torch\n", "import transformers\n", "from datasets import load_dataset\n", "from peft import LoraConfig, get_peft_model\n", "from peft import PeftModel, PeftConfig\n", "from transformers import AutoModelForCausalLM, AutoTokenizer\n", "\n", "peft_model_id = \"ybelkada/opt-6.7b-lora\"\n", "config = PeftConfig.from_pretrained(peft_model_id)\n", "model = AutoModelForCausalLM.from_pretrained(\n", " config.base_model_name_or_path,\n", " return_dict=True,\n", " load_in_8bit=True,\n", " device_map=\"auto\",\n", ")\n", "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n", "\n", "# Tải Lora adapter\n", "model = PeftModel.from_pretrained(model, peft_model_id)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MDqJWba-tpnv", "outputId": "dd65644f-6921-46e1-920c-400bd7d888cf" }, "outputs": [], "source": [ "batch = tokenizer(\"Machine learning is: \", return_tensors='pt')\n", "\n", "with torch.cuda.amp.autocast():\n", " output_tokens = model.generate(**batch, max_new_tokens=50)\n", "\n", "print('\\n\\n', tokenizer.decode(output_tokens[0], skip_special_tokens=True))" ] } ], "metadata": { "accelerator": "GPU", "colab": { "machine_shape": "hm", "provenance": [] }, "gpuClass": "standard", "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.4" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "003dc6b3dc1f4bffa7335aa5d35b1e74": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { 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vi/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb (5,059 lines of code) (raw):