L4_building_quantizer_load_from_hugging_face_hub.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vKSxr-q27Xk5"
   },
   "source": [
    "# L4-D - Building your own Quantizer: Load your Quantized Weights from Hugging Face Hub\n",
    "\n",
    "In this lesson, you will learn memory efficient model loading."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dGQrTCG58GY2"
   },
   "source": [
    "Run the next cell to import all of the functions you have used before in the previous lesson(s) of `Building your own Quantizer` to follow along with the video.\n",
    "\n",
    "- To access the `helper.py` file, you can click `File --> Open...`, on the top left."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "height": 83
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "from helper import W8A16LinearLayer, replace_linear_with_target_and_quantize, replace_linear_with_target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K_l5R7AFoVpd"
   },
   "source": [
    "## Memory Efficient Model Loading\n",
    "\n",
    "- Load [facebook/opt-125m](https://huggingface.co/facebook/opt-125m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
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    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1700734507308,
     "user": {
      "displayName": "Younes Belkada",
      "userId": "15414910276690549281"
     },
     "user_tz": -60
    },
    "height": 132,
    "id": "BeSUMWv5rOLl",
    "outputId": "81c3def4-3c3a-4ad6-d827-5a05230fe440"
   },
   "outputs": [],
   "source": [
    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
    "\n",
    "model_id = \"./models/facebook/opt-125m\"\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    model_id, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True)\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "executionInfo": {
     "elapsed": 1198,
     "status": "ok",
     "timestamp": 1700735397916,
     "user": {
      "displayName": "Younes Belkada",
      "userId": "15414910276690549281"
     },
     "user_tz": -60
    },
    "height": 64,
    "id": "WKHiXdnUoVQb"
   },
   "outputs": [],
   "source": [
    "replace_linear_with_target_and_quantize(model, \n",
    "                             W8A16LinearLayer, \n",
    "                                   [\"lm_head\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "executionInfo": {
     "elapsed": 2075,
     "status": "ok",
     "timestamp": 1700735400948,
     "user": {
      "displayName": "Younes Belkada",
      "userId": "15414910276690549281"
     },
     "user_tz": -60
    },
    "height": 30,
    "id": "w6MNxYD3rmGh"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OPTForCausalLM(\n",
       "  (model): OPTModel(\n",
       "    (decoder): OPTDecoder(\n",
       "      (embed_tokens): Embedding(50272, 768, padding_idx=1)\n",
       "      (embed_positions): OPTLearnedPositionalEmbedding(2050, 768)\n",
       "      (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "      (layers): ModuleList(\n",
       "        (0-11): 12 x OPTDecoderLayer(\n",
       "          (self_attn): OPTAttention(\n",
       "            (k_proj): W8A16LinearLayer()\n",
       "            (v_proj): W8A16LinearLayer()\n",
       "            (q_proj): W8A16LinearLayer()\n",
       "            (out_proj): W8A16LinearLayer()\n",
       "          )\n",
       "          (activation_fn): ReLU()\n",
       "          (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "          (fc1): W8A16LinearLayer()\n",
       "          (fc2): W8A16LinearLayer()\n",
       "          (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (lm_head): Linear(in_features=768, out_features=50272, bias=False)\n",
       ")"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "executionInfo": {
     "elapsed": 278,
     "status": "ok",
     "timestamp": 1700735401224,
     "user": {
      "displayName": "Younes Belkada",
      "userId": "15414910276690549281"
     },
     "user_tz": -60
    },
    "height": 47,
    "id": "xXoiKTZErq-o"
   },
   "outputs": [],
   "source": [
    "quantized_state_dict = model.state_dict()\n",
    "torch.save(quantized_state_dict, \"quantized_state_dict.pth\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The below code is for demonstration purposes only.\n",
    "- You'll need your own Hugging Face username in order for it to run.\n",
    "- You'll add your usernmae in `YOUR_HF_USERNAME = \"\"` "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "executionInfo": {
     "elapsed": 1893,
     "status": "ok",
     "timestamp": 1700735409396,
     "user": {
      "displayName": "Younes Belkada",
      "userId": "15414910276690549281"
     },
     "user_tz": -60
    },
    "id": "BPWEUwa6rtrG",
    "outputId": "f354bb37-12d3-4037-8b87-36d60cd3e0ce"
   },
   "source": [
    "```Python\n",
    "from huggingface_hub import HfApi, create_repo\n",
    "\n",
    "YOUR_HF_USERNAME = \"\"\n",
    "your_repo_id = f\"{YOUR_HF_USERNAME}/opt-125m-quantized-dlai\"\n",
    "\n",
    "api = HfApi()\n",
    "\n",
    "# create_repo(your_repo_id)\n",
    "\n",
    "api.upload_file(\n",
    " path_or_fileobj=\"quantized_state_dict.pth\",\n",
    " path_in_repo=\"quantized_state_dict.pth\",\n",
    " repo_id=your_repo_id\n",
    ")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EIsqzrFDse7I"
   },
   "source": [
    "### Load the Model in the Meta Device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "executionInfo": {
     "elapsed": 899,
     "status": "ok",
     "timestamp": 1700735433204,
     "user": {
      "displayName": "Younes Belkada",
      "userId": "15414910276690549281"
     },
     "user_tz": -60
    },
    "height": 166,
    "id": "qL8wID_ysyVD"
   },
   "outputs": [],
   "source": [
    "from transformers import OPTForCausalLM, AutoTokenizer, AutoConfig\n",
    "\n",
    "model_id = \"./models/facebook/opt-125m\"\n",
    "config = AutoConfig.from_pretrained(model_id)\n",
    "\n",
    "with torch.device(\"meta\"):\n",
    "  model = OPTForCausalLM(config)\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 727,
     "status": "ok",
     "timestamp": 1700735010719,
     "user": {
      "displayName": "Younes Belkada",
      "userId": "15414910276690549281"
     },
     "user_tz": -60
    },
    "height": 47,
    "id": "YgkfalAts3TM",
    "outputId": "3b0cc8a6-4421-40f0-e1e0-9a03ec322530"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
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      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
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      "tensor(..., device='meta', size=(3072, 768), requires_grad=True)\n",
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      "tensor(..., device='meta', size=(3072,), requires_grad=True)\n",
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      "tensor(..., device='meta', size=(768, 3072), requires_grad=True)\n",
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      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
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      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
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      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
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      "tensor(..., device='meta', size=(768, 768), requires_grad=True)\n",
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      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
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      "tensor(..., device='meta', size=(768, 768), requires_grad=True)\n",
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      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
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      "tensor(..., device='meta', size=(768, 768), requires_grad=True)\n",
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      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
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      "tensor(..., device='meta', size=(768, 768), requires_grad=True)\n",
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      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
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      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
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      "tensor(..., device='meta', size=(3072, 768), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(3072,), requires_grad=True)\n",
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      "tensor(..., device='meta', size=(768, 3072), requires_grad=True)\n",
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      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
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      "tensor(..., device='meta', size=(768, 768), requires_grad=True)\n",
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      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768, 768), requires_grad=True)\n",
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      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
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      "tensor(..., device='meta', size=(768, 768), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768, 768), requires_grad=True)\n",
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      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(3072, 768), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(3072,), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768, 3072), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768, 768), requires_grad=True)\n",
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      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768, 768), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768, 768), requires_grad=True)\n",
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      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768, 768), requires_grad=True)\n",
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      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
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      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(3072, 768), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(3072,), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768, 3072), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768, 768), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768, 768), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768, 768), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768, 768), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(3072, 768), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(3072,), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768, 3072), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768,), requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor(..., device='meta', size=(768,), requires_grad=True)\n"
     ]
    }
   ],
   "source": [
    "for param in model.parameters():\n",
    "  print(param)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "height": 30,
    "id": "tS5P5b7ptPQt"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OPTForCausalLM(\n",
       "  (model): OPTModel(\n",
       "    (decoder): OPTDecoder(\n",
       "      (embed_tokens): Embedding(50272, 768, padding_idx=1)\n",
       "      (embed_positions): OPTLearnedPositionalEmbedding(2050, 768)\n",
       "      (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "      (layers): ModuleList(\n",
       "        (0-11): 12 x OPTDecoderLayer(\n",
       "          (self_attn): OPTAttention(\n",
       "            (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "          )\n",
       "          (activation_fn): ReLU()\n",
       "          (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (lm_head): Linear(in_features=768, out_features=50272, bias=False)\n",
       ")"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "executionInfo": {
     "elapsed": 858,
     "status": "ok",
     "timestamp": 1700735436517,
     "user": {
      "displayName": "Younes Belkada",
      "userId": "15414910276690549281"
     },
     "user_tz": -60
    },
    "height": 30,
    "id": "Nu1gi0bUtOSw"
   },
   "outputs": [],
   "source": [
    "replace_linear_with_target(model, W8A16LinearLayer, [\"lm_head\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1700735438474,
     "user": {
      "displayName": "Younes Belkada",
      "userId": "15414910276690549281"
     },
     "user_tz": -60
    },
    "height": 30,
    "id": "zW-QYLm-tetF",
    "outputId": "9b19aa34-44fa-436b-fca1-edb47307f374"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OPTForCausalLM(\n",
       "  (model): OPTModel(\n",
       "    (decoder): OPTDecoder(\n",
       "      (embed_tokens): Embedding(50272, 768, padding_idx=1)\n",
       "      (embed_positions): OPTLearnedPositionalEmbedding(2050, 768)\n",
       "      (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "      (layers): ModuleList(\n",
       "        (0-11): 12 x OPTDecoderLayer(\n",
       "          (self_attn): OPTAttention(\n",
       "            (k_proj): W8A16LinearLayer()\n",
       "            (v_proj): W8A16LinearLayer()\n",
       "            (q_proj): W8A16LinearLayer()\n",
       "            (out_proj): W8A16LinearLayer()\n",
       "          )\n",
       "          (activation_fn): ReLU()\n",
       "          (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "          (fc1): W8A16LinearLayer()\n",
       "          (fc2): W8A16LinearLayer()\n",
       "          (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (lm_head): Linear(in_features=768, out_features=50272, bias=False)\n",
       ")"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1700735439874,
     "user": {
      "displayName": "Younes Belkada",
      "userId": "15414910276690549281"
     },
     "user_tz": -60
    },
    "height": 115,
    "id": "67U3w4pDtjm_"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3025ca83c83a4f82b2b539c80668b8e4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)tized_state_dict.pth:   0%|          | 0.00/166M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from huggingface_hub import hf_hub_download\n",
    "\n",
    "state_dict_cache_path = hf_hub_download(\n",
    "    \"ybelkada/opt-125m-quantized-dlai\",\n",
    "    \"quantized_state_dict.pth\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "state_dict = torch.load(state_dict_cache_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1085,
     "status": "ok",
     "timestamp": 1700735443407,
     "user": {
      "displayName": "Younes Belkada",
      "userId": "15414910276690549281"
     },
     "user_tz": -60
    },
    "height": 30,
    "id": "uOp6TKy4t0hQ",
    "outputId": "2f35af16-fbcc-4f26-cea9-e5af43fd0532"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.load_state_dict(state_dict, strict=True, assign=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lMpfk-2du3TB"
   },
   "source": [
    "- Test your model.\n",
    "- **Note:** Your generated text might be different than what you see in the video."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 12352,
     "status": "ok",
     "timestamp": 1700735485242,
     "user": {
      "displayName": "Younes Belkada",
      "userId": "15414910276690549281"
     },
     "user_tz": -60
    },
    "height": 81,
    "id": "SgLYmERGu2xu",
    "outputId": "e7200afe-9108-4b34-874b-2fa85d7f2805"
   },
   "outputs": [],
   "source": [
    "from transformers import pipeline\n",
    "\n",
    "pipe = pipeline(\"text-generation\", model=model, tokenizer=tokenizer)\n",
    "pipe(\"Hello today I am\", max_new_tokens=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "pipe = pipeline(\"text-generation\", model=model, tokenizer=tokenizer)\n",
    "pipe(\"Hello today I am giving a course about\", max_new_tokens=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": []
  }
 ],
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