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      "cell_type": "markdown",
      "source": [
        "#Baseline Model Evaluation\n",
        "\n",
        "##Base Model Selection\n",
        "We'll be using the OLMo-1B model as our base model. Here's the rationale for this choice:\n",
        "\n",
        "*  Reasonable size for fine-tuning (1B parameters)\n",
        "*  Decent general language capabilities\n",
        "*  Open source and well-documented\n",
        "*  Suitable for running on available GPU resources in Colab\n",
        "\n",
        "Let's start by evaluating its out-of-the-box performance on math problem generation. Its poor performance will motivate finetuning."
      ],
      "metadata": {
        "id": "DaHU5ajWXpyT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check GPU availability\n",
        "!nvidia-smi\n",
        "\n",
        "# Install required packages\n",
        "!pip install transformers datasets evaluate -q"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "09v6IJXHXzhT",
        "outputId": "2fd396a0-a3a3-44eb-d12f-9353d7bec601"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mon Dec 23 13:01:13 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   45C    P8               9W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n",
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.0/84.0 kB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179.3/179.3 kB\u001b[0m \u001b[31m15.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m11.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m15.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
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        "id": "B0R5Yzl_XXni",
        "outputId": "041708de-a507-4a77-aca6-bc9a9f73745b"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading tokenizer...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/5.37k [00:00<?, ?B/s]"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading model...\n"
          ]
        },
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          "output_type": "display_data",
          "data": {
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              "config.json:   0%|          | 0.00/632 [00:00<?, ?B/s]"
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          "data": {
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              "model.safetensors:   0%|          | 0.00/4.71G [00:00<?, ?B/s]"
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          "data": {
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              "generation_config.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
            ],
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              "model_id": "ed16c67fee984ac9948ebd82ca32ee1a"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "\n",
        "def setup_base_model():\n",
        "    \"\"\"Initialize the base model and tokenizer.\"\"\"\n",
        "    model_name = \"allenai/OLMo-1B-hf\"\n",
        "\n",
        "    # Load tokenizer and model with progress bars\n",
        "    print(\"Loading tokenizer...\")\n",
        "    tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side='left')\n",
        "\n",
        "    print(\"Loading model...\")\n",
        "    model = AutoModelForCausalLM.from_pretrained(\n",
        "        model_name,\n",
        "        device_map=\"auto\",  # Automatically handle device placement\n",
        "        torch_dtype=torch.float16  # Use fp16 for efficiency\n",
        "    )\n",
        "\n",
        "    if tokenizer.pad_token_id is None:\n",
        "        tokenizer.pad_token_id = tokenizer.eos_token_id\n",
        "\n",
        "    return model, tokenizer\n",
        "\n",
        "# Clear GPU memory if needed\n",
        "import gc\n",
        "gc.collect()\n",
        "torch.cuda.empty_cache()\n",
        "\n",
        "# Load model\n",
        "model, tokenizer = setup_base_model()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "message = [\"This is a simple math problem:\"]\n",
        "inputs = tokenizer(message, return_tensors=\"pt\", padding=True).to(model.device)\n",
        "response = model.generate(**inputs, max_new_tokens=100)\n",
        "print(tokenizer.decode(response[0], skip_special_tokens=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oXJUzuVRbfVD",
        "outputId": "11ceff14-302b-40dd-c970-bcb11d6e3587"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This is a simple math problem: if you have a $100 bill, and you want to buy a $10 item, you need to have $90 in your pocket.\n",
            "If you have a $100 bill, and you want to buy a $10 item, you need to have $90 in your pocket.\n",
            "If you have a $100 bill, and you want to buy a $10 item, you need to have $90 in your pocket.\n",
            "If you have a $100 bill, and you want to buy\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Try different temperatures\n",
        "for temp in [0.3, 0.7, 1.0]:\n",
        "    print(f\"\\nGeneration with temperature {temp}:\")\n",
        "    outputs = model.generate(\n",
        "        **inputs,\n",
        "        max_length=100,\n",
        "        temperature=temp,\n",
        "        do_sample=True\n",
        "    )\n",
        "    print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GAAZ1uoFpITm",
        "outputId": "1082d0dc-d16f-4a6d-e234-90ff1335c967"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Generation with temperature 0.3:\n",
            "This is a simple math problem: if you have a $100 bill, and you spend $10, you have a $90 bill. You can’t spend $90 and have a $100 bill. You can’t spend $90 and have a $90 bill. You can’t spend $90 and have a $90 bill.\n",
            "You can’t spend $90 and have a $90 bill. You can’t spend $90 and have a $90 bill. You\n",
            "\n",
            "Generation with temperature 0.7:\n",
            "This is a simple math problem: if you can use a pencil to make a straight line then you can use a pencil to draw a picture. For example, if you have a pencil you can make a line from one end of the pencil to the other. If you have a pencil, you can use it to draw a picture.\n",
            "A pencil is not a perfect tool for drawing pictures. It’s not a perfect tool for writing letters. It’s not a perfect tool for drawing pictures\n",
            "\n",
            "Generation with temperature 1.0:\n",
            "This is a simple math problem: 1 + 1 equals 2. But it’s also the first step of solving any math problem. Before we begin, we must clearly define what math is meant to solve. For any math problem, one thing is certain: it has to be solvable.\n",
            "How do you determine if something can be solved? We don’t all have the same mental model of what does or does not “solve” a math problem. Different types of mathematics require different\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test prompts\n",
        "test_prompts = [\n",
        "    \"Here's another elementary school math problem: A student\",\n",
        "    \"Here's another elementary school math problem: In a store\",\n",
        "    \"Here's another elementary school math problem: During a game\",\n",
        "]"
      ],
      "metadata": {
        "id": "BvbLxbj2aTn0"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_problems(model, tokenizer, prompts, max_length=100):\n",
        "    \"\"\"Generate math problems using the base model.\"\"\"\n",
        "    outputs = []\n",
        "\n",
        "    for prompt in prompts:\n",
        "        # Prepare input\n",
        "        inputs = tokenizer(prompt, return_tensors=\"pt\", padding=True).to(model.device)\n",
        "\n",
        "        # Generate with error handling\n",
        "        try:\n",
        "            output_ids = model.generate(\n",
        "                **inputs,\n",
        "                max_length=max_length,\n",
        "                temperature=0.7,\n",
        "                top_p=0.9,\n",
        "                do_sample=True,\n",
        "                pad_token_id=tokenizer.pad_token_id,\n",
        "                eos_token_id=tokenizer.eos_token_id\n",
        "            )\n",
        "\n",
        "            output = tokenizer.decode(output_ids[0], skip_special_tokens=True)\n",
        "            outputs.append(output)\n",
        "\n",
        "        except RuntimeError as e:\n",
        "            print(f\"Error during generation: {e}\")\n",
        "            outputs.append(\"Generation failed\")\n",
        "\n",
        "    return outputs\n",
        "\n",
        "out_examples = generate_problems(model, tokenizer, test_prompts)"
      ],
      "metadata": {
        "id": "_E_vacfAaeZ0"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for example in out_examples:\n",
        "    print(example)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y8j1iK6foce7",
        "outputId": "5f5a1608-883d-4eb1-c3d3-d841e1509af3"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Here's another elementary school math problem: A student was working on a problem in which he had to count how many apples were in a basket.\n",
            "The student said, \"I counted 12 apples in the basket.\"\n",
            "\"Then you're not counting correctly,\" the teacher responded.\n",
            "\"I'm not counting correctly,\" the student said.\n",
            "\"Then you're counting incorrectly,\" the teacher responded.\n",
            "\"I'm counting correctly,\" the student said.\n",
            "\"Then you're counting correctly,\" the\n",
            "Here's another elementary school math problem: In a store, you have 10 apples, 10 oranges, and 10 bananas. The clerk gives you a bag of apples. How many apples are in the bag?\n",
            "This is a little harder than the previous question. You're dealing with numbers, but you're not dealing with numbers you can see. So you're working with numbers that aren't real.\n",
            "Let's say that you have a bag of apples. How many apples are in the\n",
            "Here's another elementary school math problem: During a game of basketball, the team shoots the ball into the basket at a rate of 30 shots per minute. It's the school's first game of the season. The team has a record of 0-0. How many points will the team score in the first game of the season?\n",
            "This is a problem that you will encounter in many different situations. It's a problem that is often asked in real-life situations. It's a problem that\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It is clear that the model is not very good at generating valid math problems on its own. Results hint at something that resembles math problems (sometimes contain numbers, etc.) but there are problems on many fronts: repetitions, logical inconsistencies, etc.\n",
        "\n",
        "Let's try to fix this via finetuning"
      ],
      "metadata": {
        "id": "vAp0pcYEpn8Q"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "b357tfMXqKdB"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}