flan_t5_large_finetuning.ipynb

{
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      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R2uWhUF43A7W",
        "outputId": "6fc35be0-d202-4125-a4b9-5043793660e8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m244.2/244.2 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h"
          ]
        }
      ],
      "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",
      "execution_count": 2,
      "metadata": {
        "id": "k7FZiay93NXD"
      },
      "outputs": [],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "WPJSjfiw3VOk"
      },
      "outputs": [],
      "source": [
        "# The model that you want to train from the Hugging Face hub\n",
        "model_name = \"google/flan-t5-large\"\n",
        "\n",
        "# The instruction dataset to use\n",
        "dataset_name = \"mudata.json\"\n",
        "\n",
        "# Fine-tuned model name\n",
        "new_model = \"flan-t5-large-vetdataset-finetuned\"\n",
        "\n",
        "################################################################################\n",
        "# QLoRA parameters\n",
        "################################################################################\n",
        "\n",
        "# LoRA attention dimension\n",
        "lora_r = 64\n",
        "\n",
        "# Alpha parameter for LoRA scaling\n",
        "lora_alpha = 16\n",
        "\n",
        "# Dropout probability for LoRA layers\n",
        "lora_dropout = 0.1\n",
        "\n",
        "################################################################################\n",
        "# bitsandbytes parameters\n",
        "################################################################################\n",
        "\n",
        "# Activate 4-bit precision base model loading\n",
        "use_4bit = True\n",
        "\n",
        "# Compute dtype for 4-bit base models\n",
        "bnb_4bit_compute_dtype = \"float16\"\n",
        "\n",
        "# Quantization type (fp4 or nf4)\n",
        "bnb_4bit_quant_type = \"nf4\"\n",
        "\n",
        "# Activate nested quantization for 4-bit base models (double quantization)\n",
        "use_nested_quant = False\n",
        "\n",
        "################################################################################\n",
        "# TrainingArguments parameters\n",
        "################################################################################\n",
        "\n",
        "# Output directory where the model predictions and checkpoints will be stored\n",
        "output_dir = \"./results\"\n",
        "\n",
        "# Number of training epochs\n",
        "num_train_epochs = 1\n",
        "\n",
        "# Enable fp16/bf16 training (set bf16 to True with an A100)\n",
        "fp16 = False\n",
        "bf16 = False\n",
        "\n",
        "# Batch size per GPU for training\n",
        "per_device_train_batch_size = 4\n",
        "\n",
        "# Batch size per GPU for evaluation\n",
        "per_device_eval_batch_size = 4\n",
        "\n",
        "# Number of update steps to accumulate the gradients for\n",
        "gradient_accumulation_steps = 1\n",
        "\n",
        "# Enable gradient checkpointing\n",
        "gradient_checkpointing = True\n",
        "\n",
        "# Maximum gradient normal (gradient clipping)\n",
        "max_grad_norm = 0.3\n",
        "\n",
        "# Initial learning rate (AdamW optimizer)\n",
        "learning_rate = 2e-4\n",
        "\n",
        "# Weight decay to apply to all layers except bias/LayerNorm weights\n",
        "weight_decay = 0.001\n",
        "\n",
        "# Optimizer to use\n",
        "optim = \"paged_adamw_32bit\"\n",
        "\n",
        "# Learning rate schedule\n",
        "lr_scheduler_type = \"cosine\"\n",
        "\n",
        "# Number of training steps (overrides num_train_epochs)\n",
        "max_steps = -1\n",
        "\n",
        "# Ratio of steps for a linear warmup (from 0 to learning rate)\n",
        "warmup_ratio = 0.03\n",
        "\n",
        "# Group sequences into batches with same length\n",
        "# Saves memory and speeds up training considerably\n",
        "group_by_length = True\n",
        "\n",
        "# Save checkpoint every X updates steps\n",
        "save_steps = 0\n",
        "\n",
        "# Log every X updates steps\n",
        "logging_steps = 25\n",
        "\n",
        "################################################################################\n",
        "# SFT parameters\n",
        "################################################################################\n",
        "\n",
        "# Maximum sequence length to use\n",
        "max_seq_length = None\n",
        "\n",
        "# Pack multiple short examples in the same input sequence to increase efficiency\n",
        "packing = False\n",
        "\n",
        "# Load the entire model on the GPU 0\n",
        "device_map = {\"\": 0}"
      ]
    },
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          "text": [
            "Some weights of T5ForConditionalGeneration were not initialized from the model checkpoint at google/flan-t5-large and are newly initialized: ['decoder.embed_tokens.weight', 'encoder.embed_tokens.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
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              "generation_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"
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        {
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          "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 512\n",
            "  warnings.warn(\n"
          ]
        },
        {
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              "model_id": "09aee9a83bc54f3cba15a063b2e07891",
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            },
            "text/plain": [
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            ]
          },
          "metadata": {},
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        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "You're using a T5TokenizerFast 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:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1041' max='1041' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1041/1041 11:37, 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>0.383700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>0.351200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>0.036800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.036200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>125</td>\n",
              "      <td>0.015100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>0.099300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>175</td>\n",
              "      <td>0.006800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.019000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>225</td>\n",
              "      <td>0.007600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>250</td>\n",
              "      <td>0.020400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>275</td>\n",
              "      <td>0.007500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.013800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>325</td>\n",
              "      <td>0.006100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>350</td>\n",
              "      <td>0.010000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>375</td>\n",
              "      <td>0.002600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>0.007300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>425</td>\n",
              "      <td>0.004300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>450</td>\n",
              "      <td>0.016900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>475</td>\n",
              "      <td>0.002900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.007800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>525</td>\n",
              "      <td>0.004900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>550</td>\n",
              "      <td>0.012700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>575</td>\n",
              "      <td>0.002500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>0.005300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>625</td>\n",
              "      <td>0.003000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>650</td>\n",
              "      <td>0.021700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>675</td>\n",
              "      <td>0.007400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>0.010700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>725</td>\n",
              "      <td>0.003800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>750</td>\n",
              "      <td>0.008200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>775</td>\n",
              "      <td>0.010700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>0.015900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>825</td>\n",
              "      <td>0.002800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>850</td>\n",
              "      <td>0.006600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>875</td>\n",
              "      <td>0.004300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>0.008700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>925</td>\n",
              "      <td>0.004900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>950</td>\n",
              "      <td>0.004400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>975</td>\n",
              "      <td>0.003200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.011300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1025</td>\n",
              "      <td>0.004700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from transformers import AutoModelForSeq2SeqLM\n",
        "\n",
        "# Load dataset (you can process it here)\n",
        "dataset = load_dataset(\"json\", data_files=\"mudata.json\", split=\"train\")\n",
        "dataset = dataset.map(lambda example: {'text': example['question'] + example['answer']})\n",
        "\n",
        "# Load tokenizer and model with QLoRA configuration\n",
        "compute_dtype = getattr(torch, bnb_4bit_compute_dtype)\n",
        "\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=use_4bit,\n",
        "    bnb_4bit_quant_type=bnb_4bit_quant_type,\n",
        "    bnb_4bit_compute_dtype=compute_dtype,\n",
        "    bnb_4bit_use_double_quant=use_nested_quant,\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 = AutoModelForSeq2SeqLM.from_pretrained(\n",
        "    model_name,\n",
        "    quantization_config=bnb_config,\n",
        "    device_map=device_map\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=lora_alpha,\n",
        "    lora_dropout=lora_dropout,\n",
        "    r=lora_r,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\",\n",
        ")\n",
        "\n",
        "# Set training parameters\n",
        "training_arguments = TrainingArguments(\n",
        "    output_dir=output_dir,\n",
        "    num_train_epochs=num_train_epochs,\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",
        "    weight_decay=weight_decay,\n",
        "    fp16=fp16,\n",
        "    bf16=bf16,\n",
        "    max_grad_norm=max_grad_norm,\n",
        "    max_steps=max_steps,\n",
        "    warmup_ratio=warmup_ratio,\n",
        "    group_by_length=group_by_length,\n",
        "    lr_scheduler_type=lr_scheduler_type,\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=max_seq_length,\n",
        "    tokenizer=tokenizer,\n",
        "    args=training_arguments,\n",
        "    packing=packing,\n",
        ")\n",
        "\n",
        "# Train model\n",
        "trainer.train()\n",
        "\n",
        "# Save trained model\n",
        "trainer.model.save_pretrained(new_model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jl7A3sMQ4UWg",
        "outputId": "b451f2eb-3cf0-4be1-8c19-15b7f7779da0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<s>[INST] Please answer the following medical question: What is rabies? [/INST]Please answer the medical question: What is rabies? [/INST] s>[INST] Please answer the medical question: What is rabies? [/INST] [/INST] s>[INST] Please answer the medical question: What is rabies? [/INST] [/INST] s>[INST] Please answer the medical question: What is rabies? s>[INST] s>[INST] Please answer the medical question: What is rabies? [/INST] s>[INST] Please answer the medical question: What is rabies? [/INST] s>[INST] Please answer the\n"
          ]
        }
      ],
      "source": [
        "# Ignore warnings\n",
        "logging.set_verbosity(logging.CRITICAL)\n",
        "\n",
        "# Run text generation pipeline with our next model\n",
        "prompt = \"Please answer the following medical question: What is rabies?\"\n",
        "pipe = pipeline(task=\"text-generation\", model=model, tokenizer=tokenizer, max_length=200)\n",
        "result = pipe(f\"<s>[INST] {prompt} [/INST]\")\n",
        "print(result[0]['generated_text'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MbrIZKyY7nZ2",
        "outputId": "67200273-29d8-4c6b-982a-27925c7f67dd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "47469"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Empty VRAM\n",
        "del model\n",
        "del pipe\n",
        "del trainer\n",
        "import gc\n",
        "gc.collect()\n",
        "gc.collect()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "7qPN6mXk8t5x"
      },
      "outputs": [],
      "source": [
        "# Reload model in FP16 and merge it with LoRA weights\n",
        "base_model = AutoModelForSeq2SeqLM.from_pretrained(\n",
        "    model_name,\n",
        "    low_cpu_mem_usage=True,\n",
        "    return_dict=True,\n",
        "    torch_dtype=torch.float16,\n",
        "    device_map=device_map,\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\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "c6763c69115b4cff9cee9ccf382bda71",
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            "377b9262199a4bbf9c882c940cb25ad0",
            "cf9322ebf29e4cc0bd473c428d8a119d",
            "ee8fe1b782fe446e88b64a82c8b04ec0",
            "7323004054084bd6b41d90a13b1b7c7e",
            "00e26e202639494e897f977a52ea781b",
            "ae3e37c9777246d4ba849a1d3f5fec48",
            "0c170b1db6324712a413ddd57acc8c0d",
            "6d7f7b3f732844c5a4d5f00fe789151e",
            "8e4d440022b94e5291cf3e65cd7017a7",
            "7b62d9fb92aa411dbb924f3c68ac1b29",
            "e4fa7c871615491a8377ba635c631905",
            "b84680645cd14a17abf9d99f5881b332",
            "7f90fc61aba74a8a904da23762fe9143",
            "8ed98305c150422a83029fc1f8c9581f",
            "46bec9a8143e4a65ad5b6e3303412468",
            "4868aa16efd64d2399935c9f66d44f35",
            "fcc481c9e7a34d5e98d71472135a9e4f",
            "dde1a8c9a20846a7b5d5594394915fec",
            "cfaeacec412941128a648115d4d91082",
            "6b54e630764e4321bb36971689b2c4a3",
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            "12d9405e59d542de8245ef3abf2681aa",
            "946019a3e24042ffa5c1ae6f797c989f",
            "b0ac79637a3149ee8b56b4c7f3a9de22",
            "e603c77c3c5a4e08bb3fde7be1f9ab8e"
          ]
        },
        "id": "PijFD7Kf8zgG",
        "outputId": "da780032-5627-4b6f-f15e-1148c9920551"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c6763c69115b4cff9cee9ccf382bda71",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from huggingface_hub import login\n",
        "login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101,
          "referenced_widgets": [
            "a3260c4c11cf465c804c9e0ce3820dab",
            "b4b64ac1a7a6463faf2f2beae875c0cc",
            "8825c28e6c0b489995ca24ba91a68f02",
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            "e1acd32a9a454986a7ed3554930f15bb",
            "35c6997784ab4c928bb479d687ee4e63",
            "2ce05b97a7734bf092386208ea09a98e"
          ]
        },
        "id": "BjAS23Sm9Drv",
        "outputId": "53c3a16c-76b7-4ac8-8d49-5f0200357678"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a3260c4c11cf465c804c9e0ce3820dab",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/1.84G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/ahmed807762/flan-t5-large-vetdataset-finetuned/commit/aa65d20719ca40f8926d20a313937df39183b87b', commit_message='Upload tokenizer', commit_description='', oid='aa65d20719ca40f8926d20a313937df39183b87b', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.push_to_hub(new_model, use_temp_dir=False)\n",
        "tokenizer.push_to_hub(new_model, use_temp_dir=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yUxkwi6j9JBQ",
        "outputId": "74bd607f-edfd-4561-bf1f-59332f120e58"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<s>[INST] What is rabies? [/INST][INST] What is rabies? [/s] s>[INST] What is rabies? [/s] s>[INST] What is rabies? [/s] s>[INST] What is rabies? [/s] s>[INST] What is rabies? [/s] s>[INST] What is rabies? [/s] s>[INST] What is rabies? [/s] s>[INST] What is rabies? [/s] s>[INST] What is rabies? [/s] s>[INST] What is rabies? [/s] \n"
          ]
        }
      ],
      "source": [
        "# Run text generation pipeline with our next model\n",
        "prompt = \"What is rabies?\"\n",
        "pipe = pipeline(task=\"text-generation\", model=model, tokenizer=tokenizer, max_length=200)\n",
        "result = pipe(f\"<s>[INST] {prompt} [/INST]\")\n",
        "print(result[0]['generated_text'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "UNFrTcMH9bQ9"
      },
      "outputs": [],
      "source": [
        "import requests\n",
        "\n",
        "API_URL = \"https://api-inference.huggingface.co/models/ahmed807762/flan-t5-large-vetdataset-finetuned\"\n",
        "headers = {\"Authorization\": \"Bearer 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\"}\n",
        "\n",
        "def query(payload):\n",
        "\tresponse = requests.post(API_URL, headers=headers, json=payload)\n",
        "\treturn response.json()\n",
        "\n",
        "output = query({\n",
        "\t\"inputs\": \"Answer the following question: what is rabies?\",\n",
        "})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1bvbLZ3h-Gnb",
        "outputId": "1e4bc8a7-51d4-4176-eba8-c4e12d408d05"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[{'generated_text': 'What is rabies? Rabies is a disease caused by an infected animal'}]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AJmxerBx-J7_"
      },
      "outputs": [],
      "source": []
    }
  ],
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