3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb

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      "source": [
        "!pip install -q bitsandbytes datasets accelerate loralib\n",
        "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git"
      ]
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      "source": [
        "import os\n",
        "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import bitsandbytes as bnb\n",
        "from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM\n",
        "\n"
      ]
    },
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      "cell_type": "markdown",
      "metadata": {
        "id": "S65GcxNGA9kz"
      },
      "source": [
        "## Load adapters from the Hub\n",
        "\n"
      ]
    },
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      "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",
        "# Load the Lora model\n",
        "model = PeftModel.from_pretrained(model, peft_model_id)"
      ]
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      "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))"
      ]
    }
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