Code_Generation_Application.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Installing Librabries"
      ],
      "metadata": {
        "id": "AoyMguHO94_f"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
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        "colab": {
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        },
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        "outputId": "cede81ef-d927-4cd4-a4d1-6728ea55ed13"
      },
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m118.0/118.0 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.8/3.8 MB\u001b[0m \u001b[31m60.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m295.0/295.0 kB\u001b[0m \u001b[31m28.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m14.8 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[31m22.9 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[31m13.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.6/92.6 MB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.6/44.6 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m67.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.0/190.0 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[32m224.8/224.8 kB\u001b[0m \u001b[31m17.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install -q -U trl transformers accelerate sentencepiece git+https://github.com/huggingface/peft.git\n",
        "!pip install -q -U datasets bitsandbytes einops scipy wandb"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading Dataset"
      ],
      "metadata": {
        "id": "X18IrXLT-Lg6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset_name = 'theblackcat102/evol-codealpaca-v1'\n",
        "dataset = load_dataset(dataset_name, split=\"train\")\n",
        "dataset_subset = dataset.select(range(1000))"
      ],
      "metadata": {
        "colab": {
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          "height": 177,
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        "id": "arZUDbmF-N8E",
        "outputId": "806f1153-f4b5-4009-e556-4c3348f0ede7"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/2.17k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4bc0533d203342beb347cfdeec3fb38f"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/255M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "55ff377c7ef74fd2ab26098a4b8790e0"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
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              "version_minor": 0,
              "model_id": "228477b6b2b0409da5d350874b1b97ef"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2f9498a03499490199eb67e27d5d7863"
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading The Model"
      ],
      "metadata": {
        "id": "E3QwY1OL-zMV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer\n",
        "\n",
        "model_name = \"psmathur/orca_mini_3b\"\n",
        "\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "load_in_4bit=True,\n",
        "bnb_4bit_quant_type=\"nf4\",\n",
        "bnb_4bit_compute_dtype=torch.bfloat16,\n",
        ")\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "model_name,\n",
        "quantization_config=bnb_config,\n",
        "trust_remote_code=True\n",
        ")\n",
        "model.config.use_cache = False"
      ],
      "metadata": {
        "colab": {
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          "height": 273,
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        },
        "id": "xURjbA8T-2R-",
        "outputId": "6aa6107b-8421-4773-ea7c-1c96b5592fea"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/553 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1960f6a5e5f54913b26e87d3301b4ebe"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)model.bin.index.json:   0%|          | 0.00/21.8k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "9b110db2faf6424fa349c0d4a04a06b3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2ce8ccf1f79f49659438d862a348fdfc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "92826d4cba6648abb96a1b6ac00da5d6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)l-00002-of-00003.bin:   0%|          | 0.00/5.00G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c0be071cfa284edab66ec380d697e20e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)l-00003-of-00003.bin:   0%|          | 0.00/3.72G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0ad757e54af54b17b94487a0b072d50b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "eda8459a67994b01a4b3619a1222fe82"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/132 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5e767960a3b7417f8933faf19bdddcdc"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Loading Tokenizer"
      ],
      "metadata": {
        "id": "hrAzRQyc_owL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.padding_side = \"right\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 150,
          "referenced_widgets": [
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            "0c4cd288bd9548b2a62ebe4a264bc15d"
          ]
        },
        "id": "Hnx4TT5D_rZV",
        "outputId": "ee26bfce-0bbd-4e88-a61b-9ccb60180758"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/700 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1e0770d93588471f9bd9050c1260e2c0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tokenizer.model:   0%|          | 0.00/534k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6b95151c36dc42e392732b3d72e4bd69"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/208 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5f72e8f9faa345a4bd56eda3e883a765"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You are using the default legacy behaviour of the <class 'transformers.models.llama.tokenization_llama.LlamaTokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thouroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Peft"
      ],
      "metadata": {
        "id": "QXn4vmVDAz1C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import LoraConfig, get_peft_model\n",
        "\n",
        "lora_alpha = 16\n",
        "lora_dropout = 0.1\n",
        "lora_r = 64\n",
        "\n",
        "peft_config = LoraConfig(\n",
        "lora_alpha=lora_alpha,\n",
        "lora_dropout=lora_dropout,\n",
        "r=lora_r,\n",
        "bias=\"none\",\n",
        "task_type=\"CAUSAL_LM\"\n",
        ")"
      ],
      "metadata": {
        "id": "cSYG1hy4Ayfs"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading The Trainer"
      ],
      "metadata": {
        "id": "4LOd10r5A6rV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "output_dir = \"./results\"\n",
        "per_device_train_batch_size = 2\n",
        "gradient_accumulation_steps = 1\n",
        "optim = \"paged_adamw_32bit\"\n",
        "save_steps = 100\n",
        "logging_steps = 10\n",
        "learning_rate = 4e-3\n",
        "max_grad_norm = 0.3\n",
        "max_steps = -1\n",
        "warmup_ratio = 0.03\n",
        "lr_scheduler_type = \"constant\"\n",
        "\n",
        "training_arguments = TrainingArguments(\n",
        "output_dir=output_dir,\n",
        "per_device_train_batch_size=per_device_train_batch_size,\n",
        "gradient_accumulation_steps=gradient_accumulation_steps,\n",
        "optim=optim,\n",
        "save_steps=save_steps,\n",
        "logging_steps=logging_steps,\n",
        "learning_rate=learning_rate,\n",
        "fp16=True,\n",
        "max_grad_norm=max_grad_norm,\n",
        "max_steps=max_steps,\n",
        "warmup_ratio=warmup_ratio,\n",
        "group_by_length=True,\n",
        "lr_scheduler_type=lr_scheduler_type,\n",
        "num_train_epochs=1,\n",
        ")"
      ],
      "metadata": {
        "id": "MKgcEsOOA9mA"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### passing everything to the trainer"
      ],
      "metadata": {
        "id": "EeDUyFVRBQ-n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess(example):\n",
        "    return {'text': example['instruction'] + ' ' + example['output']}\n",
        "\n",
        "dataset_subset = dataset_subset.map(preprocess)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "9763125341404d07b0d8eb2ef3d57fc4",
            "0b5e8a3ef08b424793e1aea6de5574cb",
            "2f56b457b35a4e2e9f8e3e85398219e6",
            "3cc05e18e5064e1489760dd00a62607f",
            "1b64c64c80314e2385c420c9de12e55b",
            "145b1710b15a4f4aa30b2ee33c6b544b",
            "8f051d8ad8a44b36a9560507f7958d83",
            "b940844075a34b16ac6e1de4ba946a65",
            "54c5242bbb924d13b938e101b8072277",
            "84d339594a4d45219dafce166a21783e",
            "b5e3138e4840402188e0bae6c5ff7ef1"
          ]
        },
        "id": "lPaJLAe5Oc2x",
        "outputId": "dc45a5e1-9fca-4b88-bda2-4cec78c533e7"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9763125341404d07b0d8eb2ef3d57fc4"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from trl import SFTTrainer\n",
        "\n",
        "max_seq_length = 2048\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "model=model,\n",
        "train_dataset=dataset_subset,\n",
        "peft_config=peft_config,\n",
        "dataset_text_field=\"text\",\n",
        "max_seq_length=max_seq_length,\n",
        "tokenizer=tokenizer,\n",
        "args=training_arguments,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "ddde12950e2246ef8f5fb363f23a8971",
            "5ea022d7e58c44a0b78423fe9986d7b5",
            "075fe9bb77a64e51a61a5d63afab91df",
            "b78fbe75a13543ac9322caa32f807e63",
            "e20d6808fea3451b9647fa8dd8b147f5",
            "3576d7d03f34493d98640abb673b1fb8",
            "4422c05334ad49d0b06ca3cada055ec2",
            "cf58d2fbc98b41b69ecb8b5e33e74a5c",
            "322b523478f841dda2f7870e5532178c",
            "5b3fa07d697d4445b8fe732885f57a3c",
            "c70cb6c2950241508f1c7eb64bf89656"
          ]
        },
        "id": "mTMWPYw6BUOB",
        "outputId": "f658ac02-3dc4-4612-d4df-a3769f254bac"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/other.py:107: FutureWarning: prepare_model_for_int8_training is deprecated and will be removed in a future version. Use prepare_model_for_kbit_training instead.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ddde12950e2246ef8f5fb363f23a8971"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "upcasting the layer norms in float 32 for more stable training"
      ],
      "metadata": {
        "id": "4xifRCjtPfeQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for name, module in trainer.model.named_modules():\n",
        " if \"norm\" in name:\n",
        "  module = module.to(torch.float32)"
      ],
      "metadata": {
        "id": "ZaeclTWxPZz2"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training the Model"
      ],
      "metadata": {
        "id": "mhuIInfTPmDT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "N9aqmNXnPoSc",
        "outputId": "892c04be-27ea-4f9c-e505-0b9bd768f177"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='500' max='500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [500/500 31:04, Epoch 1/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>1.342400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>1.493400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>1.679500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>1.826900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.510900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>1.718800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>1.628800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>1.544200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>1.788900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>1.520800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>110</td>\n",
              "      <td>1.367100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120</td>\n",
              "      <td>1.466200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>130</td>\n",
              "      <td>1.558100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>140</td>\n",
              "      <td>1.501100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>1.649400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>160</td>\n",
              "      <td>2.090400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>170</td>\n",
              "      <td>1.665300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>180</td>\n",
              "      <td>1.673500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>190</td>\n",
              "      <td>1.734600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>1.790300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>210</td>\n",
              "      <td>1.412100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>220</td>\n",
              "      <td>1.772400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>230</td>\n",
              "      <td>1.830900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>240</td>\n",
              "      <td>1.592700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>250</td>\n",
              "      <td>1.716600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>260</td>\n",
              "      <td>1.590200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>270</td>\n",
              "      <td>1.579300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>280</td>\n",
              "      <td>1.670600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>290</td>\n",
              "      <td>1.565700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>1.584900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>310</td>\n",
              "      <td>1.727300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>320</td>\n",
              "      <td>1.604400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>330</td>\n",
              "      <td>1.656400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>340</td>\n",
              "      <td>1.970800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>350</td>\n",
              "      <td>1.603300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>360</td>\n",
              "      <td>1.627500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>370</td>\n",
              "      <td>1.490300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>380</td>\n",
              "      <td>1.716100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>390</td>\n",
              "      <td>1.736800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>1.583700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>410</td>\n",
              "      <td>1.513000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>420</td>\n",
              "      <td>1.765200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>430</td>\n",
              "      <td>1.819800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>440</td>\n",
              "      <td>1.792700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>450</td>\n",
              "      <td>1.739100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>460</td>\n",
              "      <td>1.595900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>470</td>\n",
              "      <td>1.631200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>480</td>\n",
              "      <td>1.649000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>490</td>\n",
              "      <td>1.690100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>1.614600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=500, training_loss=1.6478633155822755, metrics={'train_runtime': 1874.1426, 'train_samples_per_second': 0.534, 'train_steps_per_second': 0.267, 'total_flos': 1.41361939221888e+16, 'train_loss': 1.6478633155822755, 'epoch': 1.0})"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_to_save = trainer.model.module if hasattr(trainer.model, 'module') else trainer.model # Take care of distributed/parallel training\n",
        "model_to_save.save_pretrained(\"outputs\")"
      ],
      "metadata": {
        "id": "tXoKcyt3-30L"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lora_config = LoraConfig.from_pretrained('outputs')\n",
        "model = get_peft_model(model, lora_config)"
      ],
      "metadata": {
        "id": "U9mBRgd1--Kn"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text = '''### User: hi write for me code that adds two lists together\\n\n",
        "### Assistant: '''\n",
        "device = \"cuda:0\"\n",
        "\n",
        "inputs = tokenizer(text, return_tensors=\"pt\").to(device)\n",
        "outputs = model.generate(**inputs, max_new_tokens=1024)\n",
        "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "klsN0yq6_AgZ",
        "outputId": "d162262c-0b66-44eb-ddf5-6f4d7ab630eb"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1421: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use and modify the model generation configuration (see https://huggingface.co/docs/transformers/generation_strategies#default-text-generation-configuration )\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
            "  warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "### User: hi write for me code that adds two lists together\n",
            "\n",
            "### Assistant: \n",
            "Here's some sample code that adds two lists together:\n",
            "\n",
            "```\n",
            "list1 = [1, 2, 3]\n",
            "list2 = [4, 5, 6]\n",
            "\n",
            "result = list1 + list2\n",
            "\n",
            "print(result)\n",
            "```\n",
            "\n",
            "This will output:\n",
            "\n",
            "```\n",
            "[1, 4, 5, 6]\n",
            "```\n",
            "\n",
            "Let me know if you need any further assistance.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = '''### User: write for me for code to subtract numbers\\n\n",
        "### Assistant: '''\n",
        "device = \"cuda:0\"\n",
        "\n",
        "inputs = tokenizer(text, return_tensors=\"pt\").to(device)\n",
        "outputs = model.generate(**inputs, max_new_tokens=1024)\n",
        "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5qNIcO2VBgwf",
        "outputId": "b8fe9fd5-8a3b-4a6b-9978-7492631e317e"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "### User: write for me for code to subtract numbers\n",
            "\n",
            "### Assistant: \n",
            "Here's an example of how to subtract numbers in Python:\n",
            "\n",
            "```\n",
            "# Example code to subtract 3 and 5 in Python\n",
            "\n",
            "# First, we define the two numbers we want to subtract\n",
            "num1 = 3\n",
            "num2 = 5\n",
            "\n",
            "# Then, we use the subtraction operator (-) to subtract the two numbers\n",
            "result = num1 - num2\n",
            "\n",
            "# Finally, we print the result\n",
            "print(result)\n",
            "```\n",
            "\n",
            "The output will be:\n",
            "\n",
            "```\n",
            "-3\n",
            "```\n",
            "\n",
            "This code will work for any two numbers you want to subtract.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = '''### User: hi write for me code that does mutiplacation of matrices using numpy\\n\n",
        "### Assistant: '''\n",
        "device = \"cuda:0\"\n",
        "\n",
        "inputs = tokenizer(text, return_tensors=\"pt\").to(device)\n",
        "outputs = model.generate(**inputs, max_new_tokens=1024)\n",
        "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GEOSLZ0QCMHz",
        "outputId": "63facb8f-d270-4a7b-bcea-0bd9b39f6a70"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "### User: hi write for me code that does mutiplacation of matrices using numpy\n",
            "\n",
            "### Assistant: \n",
            "Here's an example code that does matrix multiplication using NumPy:\n",
            "\n",
            "```python\n",
            "import numpy as np\n",
            "\n",
            "# create two matrices\n",
            "A = np.array([[1, 2], [3, 4]])\n",
            "B = np.array([[5, 6], [7, 8]])\n",
            "\n",
            "# multiply matrices element-wise\n",
            "C = A * B\n",
            "\n",
            "# print result\n",
            "print(C)\n",
            "```\n",
            "\n",
            "This code creates two matrices `A` and `B` and multiplies them element-wise to produce a new matrix `C`. The resulting matrix has the same shape as `A` and `B`, but with the same number of rows as `A` and the same number of columns as `B`.\n"
          ]
        }
      ]
    }
  ]
}