"
+ ],
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ "
\n",
+ " [50/50 20:37, Epoch 2/3]\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | Step | \n",
+ " Training Loss | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 10 | \n",
+ " 1.595200 | \n",
+ "
\n",
+ " \n",
+ " | 20 | \n",
+ " 1.470100 | \n",
+ "
\n",
+ " \n",
+ " | 30 | \n",
+ " 1.305600 | \n",
+ "
\n",
+ " \n",
+ " | 40 | \n",
+ " 1.318500 | \n",
+ "
\n",
+ " \n",
+ " | 50 | \n",
+ " 1.229100 | \n",
+ "
\n",
+ " \n",
+ "
"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "TrainOutput(global_step=50, training_loss=1.3837078857421874, metrics={'train_runtime': 1262.1245, 'train_samples_per_second': 0.634, 'train_steps_per_second': 0.04, 'total_flos': 8342048247644160.0, 'train_loss': 1.3837078857421874, 'epoch': 2.82})"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 206
+ }
+ ],
+ "source": [
+ "trainer.train()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "H5c0ppfasK29"
+ },
+ "source": [
+ "During training, the model should converge nicely as follows:\n",
+ "\n",
+ "\n",
+ "\n",
+ "The `SFTTrainer` also takes care of properly saving only the adapters during training instead of saving the entire model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "v2CEgCF14M0m"
+ },
+ "outputs": [],
+ "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(folder_path + \"outputs_2\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "model.save_pretrained(folder_path + \"llama_finetuned_2\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 346
+ },
+ "id": "-txn1QQP7soa",
+ "outputId": "64456337-89f8-4830-9db2-deb81948a122"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "error",
+ "ename": "NotImplementedError",
+ "evalue": "ignored",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNotImplementedError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfolder_path\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"llama_finetuned_2\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py\u001b[0m in \u001b[0;36msave_pretrained\u001b[0;34m(self, save_directory, is_main_process, state_dict, save_function, push_to_hub, max_shard_size, safe_serialization, variant, **kwargs)\u001b[0m\n\u001b[1;32m 1713\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1714\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"is_loaded_in_4bit\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1715\u001b[0;31m raise NotImplementedError(\n\u001b[0m\u001b[1;32m 1716\u001b[0m \u001b[0;34m\"You are calling `save_pretrained` on a 4-bit converted model. This is currently not supported\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1717\u001b[0m )\n",
+ "\u001b[0;31mNotImplementedError\u001b[0m: You are calling `save_pretrained` on a 4-bit converted model. This is currently not supported"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "X71KzySsAghR"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "pKOFrRt-ea9D"
+ },
+ "outputs": [],
+ "source": [
+ "# 학습한 모델로 결과 추론\n",
+ "infer_tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, padding_side='left') # 추론 시에는 padding left\n",
+ "infer_tokenizer.pad_token = infer_tokenizer.eos_token"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "qmA4G6C64dJ4"
+ },
+ "outputs": [],
+ "source": [
+ "output_lora_config = LoraConfig.from_pretrained(folder_path + 'outputs_2')\n",
+ "output_model = get_peft_model(model, output_lora_config)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "yWEM89A48NrU"
+ },
+ "outputs": [],
+ "source": [
+ "# dataset['text']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "pgt86z-x4diG"
+ },
+ "outputs": [],
+ "source": [
+ "# text = \"Écrire un texte dans un style baroque sur la glace et le feu ### Assistant: Si j'en luis éton\"\n",
+ "# device = \"cuda:0\"\n",
+ "\n",
+ "# inputs = infer_tokenizer(text, return_tensors=\"pt\").to(device)\n",
+ "# outputs = output_model.generate(**inputs, max_new_tokens=50)\n",
+ "# print(infer_tokenizer.decode(outputs[0], skip_special_tokens=True))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "test_data[0]"
+ ],
+ "metadata": {
+ "id": "1LjyQbcul8-n"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "test_data[0]['input_with_instruction']"
+ ],
+ "metadata": {
+ "id": "jyl6zsdvluOI"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "test_data[0]['input_ids']"
+ ],
+ "metadata": {
+ "id": "5SxERpvxpy52"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "test_data[0]['attention_mask']"
+ ],
+ "metadata": {
+ "id": "0nAF1iVNqDJA"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "text = \"노래 제목이 주어지면 창의적으로 작사해주세요. 제목: Never Ending 가사: \"\n",
+ "device = 'cuda' #\"cuda:0\"\n",
+ "\n",
+ "inputs = infer_tokenizer(text, return_tensors=None)\n",
+ "inputs"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "cq87aUMylrGi",
+ "outputId": "32b6ee2b-d8c0-4b28-c2d3-212e59599eb4"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'input_ids': [1, 29871, 238, 136, 187, 238, 161, 155, 29871, 31306, 238, 173, 172, 30393, 29871, 30981, 31129, 30811, 31747, 29871, 239, 179, 192, 30708, 239, 163, 132, 239, 159, 191, 30906, 29871, 239, 161, 148, 30791, 31435, 30981, 31578, 31527, 29889, 29871, 31306, 238, 173, 172, 29901, 12391, 2796, 292, 29871, 30903, 30791, 29901, 29871], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 214
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "inputs = infer_tokenizer(text, return_tensors=\"pt\").to(device)\n",
+ "outputs = output_model.generate(**inputs, max_new_tokens=128)\n",
+ "print(infer_tokenizer.decode(outputs[0], skip_special_tokens=True))"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "lH-7ri2VplXK",
+ "outputId": "5576b512-8282-4489-a812-f9d922bc0134"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "노래 제목이 주어지면 창의적으로 작사해주세요. 제목: Never Ending 가사: 너무 잘 알고 있어요 너무 잘 알고 있어요 너무 잘 알고 있어요 너무 잘 알고 있어요 너무 잘 알고 있어요 너무 잘 알고 있어요 너무 잘\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 160,
+ "referenced_widgets": [
+ "17dec344af074c258472c079d75badd4",
+ "05c0875d6bc94d0f9da1afba9a57f84c",
+ "cea00cb303454c76abc44c2355db4968",
+ "93beba7f546648f896211bbe77ae5983",
+ "652376188e9446ba89aaa28ae9ab17bc",
+ "195e71d434ef416892768c93b04327f4",
+ "58a8c69034444a43bb3dfe29c60cf54e",
+ "cdc2234337164968a5115bc40d240250",
+ "7102ad5ecb974accbfdd502750c66adc",
+ "36299fac4277436dbeb67e58a04b4b15",
+ "5e7080a1e2fb4b30a3878c4750a326eb",
+ "00d06b402d154645a5a79cd05b786010",
+ "6ec7ab97f38c4d309e6e7c651e6ccd39",
+ "3c4189003e5948c096013124d97e6961",
+ "cbc07dce089243c9998ab4f8aa74dac3",
+ "6f4cc82d77514e9781ec04714ffffda9",
+ "2938784c205b4fcfbcba13fb34aa9288",
+ "6aefd2158bbe4f11a40672b201b8a48c",
+ "f942694a54de46fd8de1a5e97269e31c",
+ "7ef4c538df094f12be83532b8214e23e",
+ "7969ab011163417d886060e5ab0a82c6",
+ "0370b063010740f4865a097d87e83730",
+ "417a3e9df6ec4c9b928af78487e49b59",
+ "a96182b8a08e46a59d65e267aad76140",
+ "871d45a28e7845e1a765275417b27f4e",
+ "f300223c3f2e467bb49d4a101a5d86e6",
+ "721614a0486e4bbd82e1b85c0623bffc",
+ "ca1747a28ca34234b2d491517e0bfc49",
+ "4dd189dff2794e44966682f11780766a",
+ "9ade4991af57408389962234873e8c26",
+ "76380783b82d4a4496e5fe6ae1a4f459",
+ "2ad8bf0c411f4f85bb2a7e544530d18e"
+ ]
+ },
+ "id": "niyf5_Kc4ugO",
+ "outputId": "72a0d6b8-78fc-4d21-958d-6e885a50aa66"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "VBox(children=(HTML(value=' \u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# model.push_to_hub(\"llama2-qlora-finetunined-french\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush_to_hub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"llama2-lyrics-finetunined-sample2\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py\u001b[0m in \u001b[0;36mpush_to_hub\u001b[0;34m(self, repo_id, use_temp_dir, commit_message, private, use_auth_token, max_shard_size, create_pr, safe_serialization, **deprecated_kwargs)\u001b[0m\n\u001b[1;32m 812\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 813\u001b[0m \u001b[0;31m# Save all files.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 814\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwork_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_shard_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_shard_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msafe_serialization\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msafe_serialization\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 815\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 816\u001b[0m return self._upload_modified_files(\n",
+ "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py\u001b[0m in \u001b[0;36msave_pretrained\u001b[0;34m(self, save_directory, is_main_process, state_dict, save_function, push_to_hub, max_shard_size, safe_serialization, variant, **kwargs)\u001b[0m\n\u001b[1;32m 1713\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1714\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"is_loaded_in_4bit\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1715\u001b[0;31m raise NotImplementedError(\n\u001b[0m\u001b[1;32m 1716\u001b[0m \u001b[0;34m\"You are calling `save_pretrained` on a 4-bit converted model. This is currently not supported\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1717\u001b[0m )\n",
+ "\u001b[0;31mNotImplementedError\u001b[0m: You are calling `save_pretrained` on a 4-bit converted model. This is currently not supported"
+ ]
+ }
+ ],
+ "source": [
+ "# model.push_to_hub(\"llama2-qlora-finetunined-french\")\n",
+ "model.push_to_hub(\"llama2-lyrics-finetunined-sample2\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "cUmT5MRD_D9k",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 36
+ },
+ "outputId": "2459bb76-d5ec-45ec-80d2-17c821880eb4"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "'배신'"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ }
+ },
+ "metadata": {},
+ "execution_count": 230
+ }
+ ],
+ "source": [
+ "train_data[170]['title']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "train_data[170]['lyric']"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 94
+ },
+ "id": "xhPDQHkuIkSb",
+ "outputId": "69077519-0807-4000-8729-6513728874cf"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "'너 없는 세상도 눈부신걸 알아\\r\\r\\n더 이상은 너만을 바라보던\\r\\r\\n내가 아니야\\r\\r\\n그렇게 슬픈듯이 쳐다보지마\\r\\r\\n이제 더는 너에게\\r\\r\\n줄 수 있는 마음이 없어\\r\\r\\n지금 남은건 떠나는 일 뿐이란걸\\r\\r\\n고맙게도 떠나는 날\\r\\r\\n잡지 않을 너잖아\\r\\r\\n사랑했던 난 이젠 없어\\r\\r\\n너를 너를 모두 잊었어\\r\\r\\n후회해도 다신 나를 돌아보지마\\r\\r\\n함께한 우리 기억 따윈\\r\\r\\n이젠 없어 모두 버려줘\\r\\r\\n돌아서면 그만인 이별까지\\r\\r\\n아름다울 필욘 없잖아'"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ }
+ },
+ "metadata": {},
+ "execution_count": 232
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "train_data[170]['input_with_instruction']"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 94
+ },
+ "id": "zSIB14IoIzU6",
+ "outputId": "d8a022a0-bb46-4836-f8a6-93d89a055359"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "'당신은 노래 가사를 작사하는 생성 모델입니다. 주어진 제목이 따라 가사를 생성해주세요. 제목: 배신 가사: 너 없는 세상도 눈부신걸 알아 더 이상은 너만을 바라보던 내가 아니야 그렇게 슬픈듯이 쳐다보지마 이제 더는 너에게 줄 수 있는 마음이 없어 지금 남은건 떠나는 일 뿐이란걸 고맙게도 떠나는 날 잡지 않을 너잖아 사랑했던 난 이젠 없어 너를 너를 모두 잊었어 후회해도 다신 나를 돌아보지마 함께한 우리 기억 따윈 이젠 없어 모두 버려줘 돌아서면 그만인 이별까지 아름다울 필욘 없잖아'"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ }
+ },
+ "metadata": {},
+ "execution_count": 233
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
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