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            "  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",
            "  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  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"
          ]
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      "source": [
        "!pip install -q datasets\n",
        "!pip install -q git+https://github.com/huggingface/transformers\n",
        "!pip install -q bitsandbytes sentencepiece accelerate loralib\n",
        "!pip install -q -U git+https://github.com/huggingface/peft.git"
      ]
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        "import torch\n",
        "from datasets import load_dataset\n",
        "from peft import LoraConfig, get_peft_model\n",
        "from PIL import Image\n",
        "from transformers import IdeficsForVisionText2Text, AutoProcessor, Trainer, TrainingArguments, BitsAndBytesConfig"
      ],
      "metadata": {
        "id": "C6ndp1o1a1sT"
      },
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        "    load_in_4bit=True,\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.float16,\n",
        "    llm_int8_skip_modules=[\"lm_head\", \"embed_tokens\"]\n",
        ")"
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        "outputId": "dd9e9677-e1a3-406b-b2d2-ba27a182ebd9"
      },
      "execution_count": 7,
      "outputs": [
        {
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            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/281 [00:00<?, ?B/s]"
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        {
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          "data": {
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              "tokenizer_config.json:   0%|          | 0.00/1.36k [00:00<?, ?B/s]"
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        {
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          "data": {
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              "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
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              "tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
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              "added_tokens.json:   0%|          | 0.00/61.0 [00:00<?, ?B/s]"
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              "special_tokens_map.json:   0%|          | 0.00/181 [00:00<?, ?B/s]"
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        {
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          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ]
    },
    {
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        "outputId": "2f887aec-bb1d-44df-c23b-d6c4b7ef853c"
      },
      "execution_count": 8,
      "outputs": [
        {
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    {
      "cell_type": "code",
      "source": [
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oSsvMPsfcmo4",
        "outputId": "16c75144-0541-4b58-c445-acfe7236ff81"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IdeficsForVisionText2Text(\n",
              "  (model): IdeficsModel(\n",
              "    (embed_tokens): IdeficsDecoupledEmbedding(\n",
              "      num_embeddings=32000, num_additional_embeddings=2, embedding_dim=4096, partially_freeze=False\n",
              "      (additional_embedding): Embedding(2, 4096)\n",
              "    )\n",
              "    (vision_model): IdeficsVisionTransformer(\n",
              "      (embeddings): IdeficsVisionEmbeddings(\n",
              "        (patch_embedding): Conv2d(3, 1280, kernel_size=(14, 14), stride=(14, 14), bias=False)\n",
              "        (position_embedding): Embedding(257, 1280)\n",
              "      )\n",
              "      (pre_layrnorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "      (encoder): IdeficsVisionEncoder(\n",
              "        (layers): ModuleList(\n",
              "          (0-31): 32 x IdeficsVisionEncoderLayer(\n",
              "            (self_attn): IdeficsVisionAttention(\n",
              "              (k_proj): Linear4bit(in_features=1280, out_features=1280, bias=True)\n",
              "              (v_proj): Linear4bit(in_features=1280, out_features=1280, bias=True)\n",
              "              (q_proj): Linear4bit(in_features=1280, out_features=1280, bias=True)\n",
              "              (out_proj): Linear4bit(in_features=1280, out_features=1280, bias=True)\n",
              "            )\n",
              "            (layer_norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "            (mlp): IdeficsVisionMLP(\n",
              "              (activation_fn): GELUActivation()\n",
              "              (fc1): Linear4bit(in_features=1280, out_features=5120, bias=True)\n",
              "              (fc2): Linear4bit(in_features=5120, out_features=1280, bias=True)\n",
              "            )\n",
              "            (layer_norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (post_layernorm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "    )\n",
              "    (perceiver_resampler): IdeficsPerceiverResampler(\n",
              "      (blocks): ModuleList(\n",
              "        (0-5): 6 x ModuleList(\n",
              "          (0): IdeficsPerceiverAttention(\n",
              "            (context_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "            (latents_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "            (q_layer_norm): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
              "            (k_layer_norm): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
              "            (q_proj): Linear4bit(in_features=1280, out_features=1536, bias=False)\n",
              "            (k_proj): Linear4bit(in_features=1280, out_features=1536, bias=False)\n",
              "            (v_proj): Linear4bit(in_features=1280, out_features=1536, bias=False)\n",
              "            (output_proj): Linear4bit(in_features=1536, out_features=1280, bias=False)\n",
              "          )\n",
              "          (1): IdeficsMLP(\n",
              "            (ln): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "            (fc): Linear4bit(in_features=1280, out_features=5120, bias=False)\n",
              "            (act): ReLU()\n",
              "            (c_proj): Linear4bit(in_features=5120, out_features=1280, bias=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "    )\n",
              "    (layers): ModuleList(\n",
              "      (0-31): 32 x IdeficsDecoderLayer(\n",
              "        (self_attn): IdeficsAttention(\n",
              "          (q_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
              "          (k_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
              "          (v_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
              "          (o_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
              "          (rotary_emb): IdeficsEmbedding()\n",
              "        )\n",
              "        (mlp): IdeficsMLP(\n",
              "          (gate_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n",
              "          (down_proj): Linear4bit(in_features=11008, out_features=4096, bias=False)\n",
              "          (up_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n",
              "          (act_fn): SiLU()\n",
              "        )\n",
              "        (input_layernorm): IdeficsRMSNorm()\n",
              "        (post_attention_layernorm): IdeficsRMSNorm()\n",
              "      )\n",
              "    )\n",
              "    (gated_cross_attn_layers): ModuleList(\n",
              "      (0-7): 8 x IdeficsGatedCrossAttentionLayer(\n",
              "        (cross_attn): IdeficsAttention(\n",
              "          (q_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
              "          (k_proj): Linear4bit(in_features=1280, out_features=4096, bias=False)\n",
              "          (v_proj): Linear4bit(in_features=1280, out_features=4096, bias=False)\n",
              "          (o_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
              "          (rotary_emb): IdeficsEmbedding()\n",
              "          (q_layer_norm): IdeficsRMSNorm()\n",
              "          (k_layer_norm): IdeficsRMSNorm()\n",
              "        )\n",
              "        (mlp): IdeficsMLP(\n",
              "          (gate_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n",
              "          (down_proj): Linear4bit(in_features=11008, out_features=4096, bias=False)\n",
              "          (up_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n",
              "          (act_fn): SiLU()\n",
              "        )\n",
              "        (input_layernorm): IdeficsRMSNorm()\n",
              "        (post_attention_layernorm): IdeficsRMSNorm()\n",
              "        (act_cross_attn): Tanh()\n",
              "        (act_dense): Tanh()\n",
              "      )\n",
              "    )\n",
              "    (norm): IdeficsRMSNorm()\n",
              "  )\n",
              "  (lm_head): IdeficsDecoupledLinear(\n",
              "    in_features=4096, out_features=32000, out_additional_features=2, bias=False, partially_freeze=False\n",
              "    (additional_fc): Linear(in_features=4096, out_features=2, bias=False)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Inference\n",
        "def do_inference(model, processor, prompts, max_new_tokens=50):\n",
        "    tokenizer = processor.tokenizer\n",
        "    bad_words = [\"<image>\", \"<fake_token_around_image>\"]\n",
        "    if len(bad_words) > 0:\n",
        "        bad_words_ids = tokenizer(bad_words, add_special_tokens=False).input_ids\n",
        "    eos_token = \"</s>\"\n",
        "    eos_token_id = tokenizer.convert_tokens_to_ids(eos_token)\n",
        "\n",
        "    inputs = processor(prompts, return_tensors=\"pt\").to(device)\n",
        "    generated_ids = model.generate(\n",
        "        **inputs,\n",
        "        eos_token_id=[eos_token_id],\n",
        "        bad_words_ids=bad_words_ids,\n",
        "        max_new_tokens=max_new_tokens,\n",
        "        early_stopping=True\n",
        "    )\n",
        "    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]\n",
        "    print(generated_text)"
      ],
      "metadata": {
        "id": "GNBEd8_Wcmrn"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torchvision.transforms as transforms"
      ],
      "metadata": {
        "id": "SQDdyacLoOm6"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "url = \"https://hips.hearstapps.com/hmg-prod/images/cute-photos-of-cats-in-grass-1593184777.jpg\"\n",
        "prompts = [\n",
        "    url,\n",
        "    \"Question: What's on the picture? Answer:\",\n",
        "]"
      ],
      "metadata": {
        "id": "2EsJH1GJtYHv"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "##preprocessing\n",
        "def convert_to_rgb(image):\n",
        "  if image.mode == \"RGB\":\n",
        "    return image\n",
        "\n",
        "  image_rgba = image.convert(\"RGBA\")\n",
        "  background = Image.new(\"RGBA\", image_rgba.size, (255,255,255))\n",
        "  alpha_composite = Image.alpha_composite(background, image_rgba)\n",
        "  alpha_composite = alpha_composite.convert(\"RGB\")\n",
        "  return alpha_composite\n",
        "\n",
        "def ds_transforms(example_batch):\n",
        "  image_size = processor.image_processor.image_size\n",
        "  image_mean = processor.image_processor.image_mean\n",
        "  image_std = processor.image_processor.image_std\n",
        "\n",
        "  image_transform = transforms.Compose([\n",
        "      convert_to_rgb,\n",
        "      transforms.RandomResizedCrop((image_size, image_size), scale=(0.9, 1.0), interpolation=transforms.InterpolationMode.BICUBIC),\n",
        "      transforms.ToTensor(),\n",
        "      transforms.Normalize(mean=image_mean, std=image_std)\n",
        "  ])\n",
        "\n",
        "  prompts = []\n",
        "  for i in range(len(example_batch['caption'])):\n",
        "    caption = example_batch['caption'][i].split(\".\")[0]\n",
        "    prompts.append(\n",
        "        [\n",
        "            example_batch['image_url'][i],\n",
        "            f\"Question: What's on the picture? Answer: This is {example_batch['name']}. {caption}</s>\",\n",
        "        ],\n",
        "    )\n",
        "  inputs = processor(prompts, transform=image_transform, return_tensors=\"pt\").to(device)\n",
        "  inputs[\"labels\"] = inputs[\"input_ids\"]\n",
        "  return inputs"
      ],
      "metadata": {
        "id": "zONhbOb6cmub"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Load and prepare the data\n",
        "ds = load_dataset(\"TheFusion21/PokemonCards\")\n",
        "ds = ds[\"train\"].train_test_split(test_size=0.002)\n",
        "train_ds = ds[\"train\"]\n",
        "eval_ds = ds[\"test\"]\n",
        "train_ds.set_transform(ds_transforms)\n",
        "eval_ds.set_transform(ds_transforms)"
      ],
      "metadata": {
        "id": "Azm8YpyjnFvF"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = checkpoint.split(\"/\")[1]\n",
        "config = LoraConfig(\n",
        "    r = 16,\n",
        "    lora_alpha = 32,\n",
        "    target_modules = [\"q_proj\", \"k_proj\", \"v_proj\"],\n",
        "    lora_dropout = 0.05,\n",
        "    bias=\"none\"\n",
        ")"
      ],
      "metadata": {
        "id": "w3S0BxmEnFx1"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = get_peft_model(model, config)"
      ],
      "metadata": {
        "id": "MEXQiJ3wp2cq"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.print_trainable_parameters()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fri0SBotp2fb",
        "outputId": "60ed8b1c-7e82-420a-e6e9-602c950a8758"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 19,750,912 || all params: 8,949,430,544 || trainable%: 0.2206946230030432\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_args = TrainingArguments(\n",
        "    output_dir = f\"{model_name}-PokemonCards\",\n",
        "    learning_rate = 2e-4,\n",
        "    fp16 = True,\n",
        "    per_device_train_batch_size = 2,\n",
        "    per_device_eval_batch_size = 2,\n",
        "    gradient_accumulation_steps = 8,\n",
        "    dataloader_pin_memory = False,\n",
        "    save_total_limit = 3,\n",
        "    evaluation_strategy =\"steps\",\n",
        "    save_strategy = \"steps\",\n",
        "    eval_steps = 10,\n",
        "    save_steps = 25,\n",
        "    max_steps = 25,\n",
        "    logging_steps = 5,\n",
        "    remove_unused_columns = False,\n",
        "    push_to_hub=False,\n",
        "    label_names = [\"labels\"],\n",
        "    load_best_model_at_end = False,\n",
        "    report_to = \"none\",\n",
        "    optim = \"paged_adamw_8bit\",\n",
        ")"
      ],
      "metadata": {
        "id": "CwHnorLfnF09"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Trainer(\n",
        "    model = model,\n",
        "    args = training_args,\n",
        "    train_dataset = train_ds,\n",
        "    eval_dataset = eval_ds\n",
        ")"
      ],
      "metadata": {
        "id": "5DFRl60Icmz3"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        },
        "id": "IahTFbPEqXFu",
        "outputId": "c6679ac6-17a8-49f8-83b4-29f48addd2e2"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='25' max='25' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [25/25 05:17, Epoch 0/1]\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",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.719300</td>\n",
              "      <td>0.767722</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.742600</td>\n",
              "      <td>0.721967</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=25, training_loss=0.7252591323852539, metrics={'train_runtime': 331.9892, 'train_samples_per_second': 1.205, 'train_steps_per_second': 0.075, 'total_flos': 1942680656828736.0, 'train_loss': 0.7252591323852539, 'epoch': 0.03})"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "url = \"https://images.pokemontcg.io/pop6/2_hires.png\""
      ],
      "metadata": {
        "id": "BsOZ4gqZyvGM"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prompts = [\n",
        "    url,\n",
        "    \"Question: What's on the picture? Answer:\",\n",
        "]"
      ],
      "metadata": {
        "id": "sFyoluVryp2a"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "do_inference(model, processor, prompts, max_new_tokens=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cdtA90mRziCn",
        "outputId": "bb35312b-37b0-4c3a-d1a3-bc48bc4ff6de"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:433: UserWarning: `num_beams` is set to 1. However, `early_stopping` is set to `True` -- this flag is only used in beam-based generation modes. You should set `num_beams>1` or unset `early_stopping`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Question: What's on the picture? Answer: This is ['Lucario', 'Gardevoir']. A Basic Pokemon Card of type Darkness with the title Lucario and 90 HP of rarity Rare Holo from the set Black Star Promos and the flavor text: It is said that Lucario is a Pokemon that can only be found in the deepest parts of the forest\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import locale\n",
        "locale.getpreferredencoding = lambda: \"UTF-8\""
      ],
      "metadata": {
        "id": "m-g58M_40Ftu"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Push to hub"
      ],
      "metadata": {
        "id": "Y4cfLqy5z3RI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli login"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xe2JNV5Vzqls",
        "outputId": "cc3c9f88-1fe2-4005-ade3-e01221d83729"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Token: \n",
            "Add token as git credential? (Y/n) n\n",
            "Token is valid (permission: write).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.push_to_hub(f\"{model_name}-PokemonCards\", private=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118,
          "referenced_widgets": [
            "df5f6a371e2d44f090daacc305eaa032",
            "d3fffc9ba5784552b322d1893f7f993b",
            "7ab9f7c87164422aafbb7d9185f3fd71",
            "4722f6076a6f45a49291cd51620c0f09",
            "24c6c1f2d099473abaabe0cadeaf9313",
            "161ff09048a747f7a9634b3b767f2ea8",
            "ac71b12ed2814f54810cbd98521f9544",
            "059ce7ab6000428e94a0018e3c19692f",
            "6503457190624ea390ae10f4ae740975",
            "9875a1bf36d744208677252e4013af6f",
            "222e71b70f35447bad800807784797ef"
          ]
        },
        "id": "XFsLlsg60ARF",
        "outputId": "556dcbc3-8a9c-43e8-bdfa-40bfcca02b92"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_model.safetensors:   0%|          | 0.00/79.1M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "df5f6a371e2d44f090daacc305eaa032"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/skuma307/idefics-9b-PokemonCards/commit/258ff93d25bd52369255a3b339d2a492b3eff3c7', commit_message='Upload model', commit_description='', oid='258ff93d25bd52369255a3b339d2a492b3eff3c7', pr_url=None, pr_revision=None, pr_num=None)"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lNyohH7B1gx5"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}