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            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/244.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.4/244.2 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m244.2/244.2 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m72.9/72.9 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.5/92.5 MB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.4/7.4 MB\u001b[0m \u001b[31m13.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.4/77.4 kB\u001b[0m \u001b[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m65.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m547.8/547.8 kB\u001b[0m \u001b[31m43.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39.9/39.9 MB\u001b[0m \u001b[31m13.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.9/64.9 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[32m194.1/194.1 kB\u001b[0m \u001b[31m16.5 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",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install -q accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import torch\n",
        "from datasets import load_dataset\n",
        "from transformers import (\n",
        "    AutoModelForCausalLM,\n",
        "    AutoTokenizer,\n",
        "    BitsAndBytesConfig,\n",
        "    HfArgumentParser,\n",
        "    TrainingArguments,\n",
        "    pipeline,\n",
        "    logging,\n",
        ")\n",
        "from peft import LoraConfig, PeftModel\n",
        "from trl import SFTTrainer"
      ],
      "metadata": {
        "id": "nAMzy_0FtaUZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load dataset (you can process it here)\n",
        "model_name=\"NousResearch/Llama-2-7b-chat-hf\"\n",
        "dataset = load_dataset(\"synapsecai/synthetic-sensitive-information\",split=\"train[:50%]\")\n",
        "use_4bit = True\n",
        "# Load tokenizer and model with QLoRA configuration\n",
        "compute_dtype = getattr(torch, \"float16\")\n",
        "\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=\"float16\",\n",
        "    bnb_4bit_use_double_quant=False,\n",
        ")\n",
        "\n",
        "# Check GPU compatibility with bfloat16\n",
        "if compute_dtype == torch.float16 and use_4bit:\n",
        "    major, _ = torch.cuda.get_device_capability()\n",
        "    if major >= 8:\n",
        "        print(\"=\" * 80)\n",
        "        print(\"Your GPU supports bfloat16: accelerate training with bf16=True\")\n",
        "        print(\"=\" * 80)\n",
        "\n",
        "# Load base model\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    quantization_config=bnb_config,\n",
        "    device_map={\"\": 0}\n",
        ")\n",
        "model.config.use_cache = False\n",
        "model.config.pretraining_tp = 1\n",
        "\n",
        "# Load LLaMA tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.padding_side = \"right\" # Fix weird overflow issue with fp16 training\n",
        "\n",
        "# Load LoRA configuration\n",
        "peft_config = LoraConfig(\n",
        "    lora_alpha=8,\n",
        "    lora_dropout=0.01,\n",
        "    r=32,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\",\n",
        ")\n",
        "\n",
        "# Set training parameters\n",
        "training_arguments = TrainingArguments(\n",
        "    output_dir=\"./results\",\n",
        "    num_train_epochs=1,\n",
        "    per_device_train_batch_size=8,\n",
        "    per_device_eval_batch_size = 4,\n",
        "    gradient_accumulation_steps=1,\n",
        "    optim=\"paged_adamw_32bit\",\n",
        "    save_steps=0,\n",
        "    logging_steps=25,\n",
        "    learning_rate=2e-4,\n",
        "    weight_decay=0.001,\n",
        "    fp16=False,\n",
        "    bf16=False,\n",
        "    max_grad_norm=0.3,\n",
        "    max_steps=-1,\n",
        "    warmup_ratio=0.03,\n",
        "    group_by_length=True,\n",
        "    lr_scheduler_type= \"cosine\",\n",
        "    report_to=\"tensorboard\"\n",
        ")\n",
        "\n",
        "# Set supervised fine-tuning parameters\n",
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    train_dataset=dataset,\n",
        "    peft_config=peft_config,\n",
        "    dataset_text_field=\"text\",\n",
        "    max_seq_length=None,\n",
        "    tokenizer=tokenizer,\n",
        "    args=training_arguments,\n",
        "    packing=False,\n",
        ")\n",
        "\n",
        "# Train model\n",
        "trainer.train()"
      ],
      "metadata": {
        "id": "OJXpOgBFuSrc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "c52bd945f28546038c9f5c7304aef0fc",
            "4a849c1b7c424734842a9ddb7fb3fab5",
            "2fdf34bcd4eb44479c860669eb7e05b5",
            "9739b990c54549778d808273ce5550e1",
            "f22d2a0cbf9448eaae9107f149b89209",
            "a445f21b64f64d5eb410eb0aafa38fe7",
            "b00f856972c84944953b2af856936b3a",
            "8652be37afd540b485a0179fd4662fcc",
            "1b396c20fd12450fba7220a3b00db5dd",
            "3002b51b31f14639b7d39ef5fd044635",
            "ca49732fd1f74cca960d1a44832a2ba6",
            "a9aafb3ac78a45279f3bfc9ecbeacc4c",
            "14b837e5ecbd49b88f9a9e14de3e1b55",
            "da7bbc38cf8d4fc1b30684818a8ca7a1",
            "7fd6e97cfb5b4eed88d68db22dee1599",
            "69ac7e667da746cb8e3ddb9497713bb4",
            "672962709b7142fa9e3091611e604f42",
            "b9f974c97b074c1ba9058cdc30de8b9e",
            "1e3f58fe3b5240fbb601f13e6450b817",
            "afd88bdb6e75455fb9fe398d85480dd3",
            "c94cafcce2354210bae7ee78cbb10b30",
            "34ee65d2cec94d598a90d6d05bdb53e2"
          ]
        },
        "outputId": "2d6d5271-6b09-431d-d32d-4d8fbe1473af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c52bd945f28546038c9f5c7304aef0fc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/other.py:102: 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",
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:159: UserWarning: You didn't pass a `max_seq_length` argument to the SFTTrainer, this will default to 1024\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/7465 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a9aafb3ac78a45279f3bfc9ecbeacc4c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='934' max='934' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [934/934 56:36, 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>25</td>\n",
              "      <td>2.470800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>0.979100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>0.774600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.518900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>125</td>\n",
              "      <td>0.671800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>0.487300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>175</td>\n",
              "      <td>0.643600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.472600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>225</td>\n",
              "      <td>0.626100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>250</td>\n",
              "      <td>0.456800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>275</td>\n",
              "      <td>0.617800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.469400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>325</td>\n",
              "      <td>0.603200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>350</td>\n",
              "      <td>0.427800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>375</td>\n",
              "      <td>0.599300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>0.448900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>425</td>\n",
              "      <td>0.606100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>450</td>\n",
              "      <td>0.441100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>475</td>\n",
              "      <td>0.594700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.441700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>525</td>\n",
              "      <td>0.580100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>550</td>\n",
              "      <td>0.434800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>575</td>\n",
              "      <td>0.574900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>0.445700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>625</td>\n",
              "      <td>0.571400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>650</td>\n",
              "      <td>0.402700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>675</td>\n",
              "      <td>0.558000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>0.410600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>725</td>\n",
              "      <td>0.571100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>750</td>\n",
              "      <td>0.414300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>775</td>\n",
              "      <td>0.565200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>0.404200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>825</td>\n",
              "      <td>0.557800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>850</td>\n",
              "      <td>0.399900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>875</td>\n",
              "      <td>0.567400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>0.421800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>925</td>\n",
              "      <td>0.526600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=934, training_loss=0.5860846726746324, metrics={'train_runtime': 3405.1378, 'train_samples_per_second': 2.192, 'train_steps_per_second': 0.274, 'total_flos': 1.948109272485888e+16, 'train_loss': 0.5860846726746324, 'epoch': 1.0})"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_model = \"Llama-2-7b-chat-finetune_TEXT2SQL\""
      ],
      "metadata": {
        "id": "Zssxq6-R95BD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save trained model\n",
        "trainer.model.save_pretrained(new_model)"
      ],
      "metadata": {
        "id": "aQNaFYp40M6k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Ignore warnings\n",
        "logging.set_verbosity(logging.CRITICAL)\n",
        "\n",
        "# Run text generation pipeline with our next model\n",
        "prompt = \"\"\"### Context: CREATE TABLE students (\n",
        "    student_id INT,\n",
        "    name VARCHAR,\n",
        "    batch_year INT,\n",
        "    cgpa FLOAT\n",
        ")\n",
        "### Question: Write a SQL query to get students from the 2020 batch whose CGPA was lower than 7.0\n",
        "### Answer:\"\"\"\n",
        "\n",
        "pipe = pipeline(task=\"text-generation\", model=model, tokenizer=tokenizer, max_length=200)\n",
        "result = pipe(f\"<s>[INST] {prompt} [/INST]\")\n",
        "\n",
        "# Extract only the first answer\n",
        "generated_text = result[0]['generated_text']\n",
        "answer_start = generated_text.find(\"### Answer:\") + len(\"### Answer:\")\n",
        "answer_end = generated_text.find(\"###\", answer_start)\n",
        "if answer_end == -1:  # If there's no next \"###\", take the rest of the string\n",
        "    answer_end = len(generated_text)\n",
        "\n",
        "sql_query = generated_text[answer_start:answer_end].strip()\n",
        "print(sql_query)"
      ],
      "metadata": {
        "id": "frlSLPin4IJ4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9247ecc0-c9cc-4960-ece5-f964f63ea489"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[/INST]  SELECT student_id FROM students WHERE batch_year = 2020 AND cgpa < 7.0 ORDER BY name\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Empty VRAM\n",
        "del model\n",
        "del pipe\n",
        "del trainer\n",
        "import gc\n",
        "gc.collect()\n",
        "gc.collect()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mkQCviG0Zta-",
        "outputId": "803ef85d-ec47-4586-a5bb-e0075f37b35e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20933"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Reload model in FP16 and merge it with LoRA weights\n",
        "base_model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    low_cpu_mem_usage=True,\n",
        "    return_dict=True,\n",
        "    torch_dtype=torch.float16,\n",
        "    device_map={\"\": 0},\n",
        ")\n",
        "model = PeftModel.from_pretrained(base_model, new_model)\n",
        "model = model.merge_and_unload()\n",
        "\n",
        "# Reload tokenizer to save it\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.padding_side = \"right\""
      ],
      "metadata": {
        "id": "QQn30cRtAZ-P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103,
          "referenced_widgets": [
            "34ee38afd5614d5cb2ee99827d715e6e",
            "d8e133a407044a7090673f073d00c4fe",
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            "2b4c5889579c49829fe87b2f58147cef",
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            "a841ace69a5942d68498986747df2c44",
            "c93ce750dc844fd7a5719ae84dc7b2e5"
          ]
        },
        "outputId": "481b242d-8e29-470a-931a-2edfae08237c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "34ee38afd5614d5cb2ee99827d715e6e"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import locale\n",
        "locale.getpreferredencoding = lambda: \"UTF-8\""
      ],
      "metadata": {
        "id": "Z8VUygTdqVnJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli login\n",
        "\n",
        "model.push_to_hub(\"Sharpaxis/Llama-2-7b-chat-finetune_TEXT2SQL\", check_pr=True)\n",
        "tokenizer.push_to_hub(\"Sharpaxis/Llama-2-7b-chat-finetune_TEXT2SQL\", check_pr=True)\n"
      ],
      "metadata": {
        "id": "x-xPb-_qB0dz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570,
          "referenced_widgets": [
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            "23df86ed3c7d47c390ceb64950a1b373",
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        },
        "outputId": "cda27eb2-3b8d-4081-d51a-25d03cf967f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "    A token is already saved on your machine. Run `huggingface-cli whoami` to get more information or `huggingface-cli logout` if you want to log out.\n",
            "    Setting a new token will erase the existing one.\n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Enter your token (input will not be visible): \n",
            "Add token as git credential? (Y/n) Y\n",
            "Token is valid (permission: write).\n",
            "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
            "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
            "\n",
            "git config --global credential.helper store\n",
            "\n",
            "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
            "Token has not been saved to git credential helper.\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        },
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