ch06/01_main-chapter-code/load-finetuned-model.ipynb

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    "<table style=\"width:100%\">\n",
    "<tr>\n",
    "<td style=\"vertical-align:middle; text-align:left;\">\n",
    "<font size=\"2\">\n",
    "Supplementary code for the <a href=\"http://mng.bz/orYv\">Build a Large Language Model From Scratch</a> book by <a href=\"https://sebastianraschka.com\">Sebastian Raschka</a><br>\n",
    "<br>Code repository: <a href=\"https://github.com/rasbt/LLMs-from-scratch\">https://github.com/rasbt/LLMs-from-scratch</a>\n",
    "</font>\n",
    "</td>\n",
    "<td style=\"vertical-align:middle; text-align:left;\">\n",
    "<a href=\"http://mng.bz/orYv\"><img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/cover-small.webp\" width=\"100px\"></a>\n",
    "</td>\n",
    "</tr>\n",
    "</table>"
   ]
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   "source": [
    "# Load And Use Finetuned Model"
   ]
  },
  {
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   "source": [
    "This notebook contains minimal code to load the finetuned model that was created and saved in chapter 6 via [ch06.ipynb](ch06.ipynb)."
   ]
  },
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     "text": [
      "tiktoken version: 0.7.0\n",
      "torch version: 2.4.0\n"
     ]
    }
   ],
   "source": [
    "from importlib.metadata import version\n",
    "\n",
    "pkgs = [\n",
    "    \"tiktoken\",    # Tokenizer\n",
    "    \"torch\",       # Deep learning library\n",
    "]\n",
    "for p in pkgs:\n",
    "    print(f\"{p} version: {version(p)}\")"
   ]
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  {
   "cell_type": "code",
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   "source": [
    "from pathlib import Path\n",
    "\n",
    "finetuned_model_path = Path(\"review_classifier.pth\")\n",
    "if not finetuned_model_path.exists():\n",
    "    print(\n",
    "        f\"Could not find '{finetuned_model_path}'.\\n\"\n",
    "        \"Run the `ch06.ipynb` notebook to finetune and save the finetuned model.\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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    "id": "fb02584a-5e31-45d5-8377-794876907bc6"
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   "source": [
    "from previous_chapters import GPTModel\n",
    "\n",
    "\n",
    "BASE_CONFIG = {\n",
    "    \"vocab_size\": 50257,     # Vocabulary size\n",
    "    \"context_length\": 1024,  # Context length\n",
    "    \"drop_rate\": 0.0,        # Dropout rate\n",
    "    \"qkv_bias\": True         # Query-key-value bias\n",
    "}\n",
    "\n",
    "model_configs = {\n",
    "    \"gpt2-small (124M)\": {\"emb_dim\": 768, \"n_layers\": 12, \"n_heads\": 12},\n",
    "    \"gpt2-medium (355M)\": {\"emb_dim\": 1024, \"n_layers\": 24, \"n_heads\": 16},\n",
    "    \"gpt2-large (774M)\": {\"emb_dim\": 1280, \"n_layers\": 36, \"n_heads\": 20},\n",
    "    \"gpt2-xl (1558M)\": {\"emb_dim\": 1600, \"n_layers\": 48, \"n_heads\": 25},\n",
    "}\n",
    "\n",
    "CHOOSE_MODEL = \"gpt2-small (124M)\"\n",
    "\n",
    "BASE_CONFIG.update(model_configs[CHOOSE_MODEL])\n",
    "\n",
    "# Initialize base model\n",
    "model = GPTModel(BASE_CONFIG)"
   ]
  },
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   "source": [
    "import torch\n",
    "\n",
    "# Convert model to classifier as in section 6.5 in ch06.ipynb\n",
    "num_classes = 2\n",
    "model.out_head = torch.nn.Linear(in_features=BASE_CONFIG[\"emb_dim\"], out_features=num_classes)\n",
    "\n",
    "# Then load pretrained weights\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model.load_state_dict(torch.load(\"review_classifier.pth\", map_location=device, weights_only=True))\n",
    "model.to(device)\n",
    "model.eval();"
   ]
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   "cell_type": "code",
   "execution_count": 5,
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    "id": "a1fd174e-9555-46c5-8780-19b0aa4f26e5"
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   "source": [
    "import tiktoken\n",
    "\n",
    "tokenizer = tiktoken.get_encoding(\"gpt2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   "source": [
    "# This function was implemented in ch06.ipynb\n",
    "def classify_review(text, model, tokenizer, device, max_length=None, pad_token_id=50256):\n",
    "    model.eval()\n",
    "\n",
    "    # Prepare inputs to the model\n",
    "    input_ids = tokenizer.encode(text)\n",
    "    supported_context_length = model.pos_emb.weight.shape[0]\n",
    "\n",
    "    # Truncate sequences if they too long\n",
    "    input_ids = input_ids[:min(max_length, supported_context_length)]\n",
    "\n",
    "    # Pad sequences to the longest sequence\n",
    "    input_ids += [pad_token_id] * (max_length - len(input_ids))\n",
    "    input_tensor = torch.tensor(input_ids, device=device).unsqueeze(0) # add batch dimension\n",
    "\n",
    "    # Model inference\n",
    "    with torch.no_grad():\n",
    "        logits = model(input_tensor.to(device))[:, -1, :]  # Logits of the last output token\n",
    "    predicted_label = torch.argmax(logits, dim=-1).item()\n",
    "\n",
    "    # Return the classified result\n",
    "    return \"spam\" if predicted_label == 1 else \"not spam\""
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "spam\n"
     ]
    }
   ],
   "source": [
    "text_1 = (\n",
    "    \"You are a winner you have been specially\"\n",
    "    \" selected to receive $1000 cash or a $2000 award.\"\n",
    ")\n",
    "\n",
    "print(classify_review(\n",
    "    text_1, model, tokenizer, device, max_length=120\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "78472e05-cb4e-4ec4-82e8-23777aa90cf8",
   "metadata": {
    "colab": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not spam\n"
     ]
    }
   ],
   "source": [
    "text_2 = (\n",
    "    \"Hey, just wanted to check if we're still on\"\n",
    "    \" for dinner tonight? Let me know!\"\n",
    ")\n",
    "\n",
    "print(classify_review(\n",
    "    text_2, model, tokenizer, device, max_length=120\n",
    "))"
   ]
  }
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