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"datasets:\n", "\n", "* https://huggingface.co/datasets/heliosbrahma/mental_health_chatbot_dataset\n", "* https://huggingface.co/datasets/mpingale/mental-health-chat-dataset\n", "\n", "\n", "base model:\n", "\n", "* https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct\n" ], "metadata": { "id": "sMGyMDDF-C-8" } }, { "cell_type": "markdown", "source": [ "# Dataset Preprocessing" ], "metadata": { "id": "2YeE6GjiH-B4" } }, { "cell_type": "code", "source": [ "!pip install datasets\n", "!pip install peft" ], "metadata": { "id": "1LBNZdky_Uo1", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "247b2548-27ff-4086-96e2-17d34a59b04b" }, "execution_count": 14, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (2.20.0)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.15.4)\n", "Requirement already satisfied: numpy>=1.17 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"execution_count": 1, "metadata": { "id": "REoqg-8C8RtN", "colab": { "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ "7c57de542a934cb0b91fb2b0e8c88e7b", "e09a6eac62cf4aa99168d0e50fec5314", "4751982e3bf64953857f00cb23d57ecc", "4ed30f1c561e4da2bfe2526e4176dc25", "801b311b93d94774a3f38e8ea00e8c49", "932ec945bb284f56b55d7853b7ba31eb", "c913025b0e76407e961e89c335ab1753", "56eef714b9c5492885577f21e9d62733", "1c5a50faaf8a49f49be022b5c2876d22", "a7973537d82841f0aacf3bf58b5c5f1f", "959888c3ca634156b123f3770e3c060b", "fe24689cc1db47a19a678366d7d6ad74", "9d3b5ede98d34346a57e3770b573a1db", "7e33f46e49b84ad0b3c8b996c87bea1a", "23980ee9ed16468c98a2506af3a4604c", "60b988d2a7ab4a0e908bb607e9040e61", "20016c80b6484f8aa3d95c968139277e", "cab2fd703e514790933d75bdf92a27a2", "a317772454ac4e2aa6b58984540e78ae", "fbe36b5630e0405fade3bcccaf132f04", "50a020332d8e4afbbaf5c86c5a316a8e", "f388c680bd57423297e98246a4bd4eb0", "8acc093a26e34ccc9e4e7c0252f4716b", "07018e8f53474ca390b9922cd7f71661", "7b2d8936c0cf47e5a8511d7f4d07be53", "b6fcbc49147149e4904aade7ed741ec2", "68f6075448b9401ca20c182bc1ebbed3", "c09de1f282e64b478aa8d98377b9d834", "a7cbf199909643db8304d3f30d5421ee", "4dfbdf6c802d45c8bf14cbb440e0f536", "7af6e0eb58b542eaa45ed11a1991f6a5", "2822b24ab68f428fb5d6f20da28ea31d" ] }, "outputId": "0e54b345-3433-40aa-da22-91b254c8f722" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "VBox(children=(HTML(value='
:\\s*|:\\s*', example[\"text\"])\n", " question = split_text[1].strip()\n", " answer = split_text[2].strip()\n", " contents = [prompt, question, answer]\n", " message = [{\"role\": template[\"role\"], \"content\": template[\"content\"].format(content)} for template, content in zip(message_template, contents)]\n", " message = tokenizer.apply_chat_template(message, return_tensors=\"pt\")\n", " return {\"text\":message[0]}\n", "\n", "\n", "tokenized_dataset_1 = dataset_1.map(tokenize)\n", "print(tokenizer.decode(tokenized_dataset_1[\"text\"][0]))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fD9Vyb_mTo1b", "outputId": "2eeda85a-287b-43bb-c6ea-a2c667674b61" }, "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n", "\n", "You are a therapy chatbot, designed to offer emotional support and companionship to users seeking a listening ear. Your purpose is to engage in conversations that provide comfort, offer insights based on therapeutic principles, and suggest resources when appropriate. You need to act in a friendly and empathetic manner, ensuring that users feel heard and supported during their interactions with you.<|eot_id|><|start_header_id|>user<|end_header_id|>\n", "\n", "What is a panic attack?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n", "\n", "Panic attacks come on suddenly and involve intense and often overwhelming fear. They’re accompanied by very challenging physical symptoms, like a racing heartbeat, shortness of breath, or nausea. Unexpected panic attacks occur without an obvious cause. Expected panic attacks are cued by external stressors, like phobias. Panic attacks can happen to anyone, but having more than one may be a sign of panic disorder, a mental health condition characterized by sudden and repeated panic attacks.<|eot_id|>\n" ] } ] }, { "cell_type": "markdown", "source": [ "Removing Duplicates From Dataset 2\n", "\n", "https://discuss.huggingface.co/t/how-can-i-drop-duplicates-on-datasets-module/15369" ], "metadata": { "id": "ndMHgkK_jiPg" } }, { "cell_type": "code", "source": [ "import pandas as pd\n", "\n", "\n", "df = pd.DataFrame(dataset_2)\n", "print(\"Number of unique questions:\", df['questionID'].max() + 1) # Because questionID begins at 0\n", "\n", "# Sort the DataFrame by 'question' and 'views' in descending order\n", "df_filtered = df.loc[df.groupby('questionID')['views'].idxmax()].reset_index()\n", "print(\"Question Answer pairs after filter:\", df_filtered.shape[0])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "g1FTeYhrjMEu", "outputId": "59d7bdbc-dda6-439f-f723-9cdce74febba" }, "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Number of unique questions: 940\n", "Question Answer pairs after filter: 940\n" ] } ] }, { "cell_type": "code", "source": [ "from datasets import Dataset\n", "\n", "\n", "dataset_2 = Dataset.from_pandas(df_filtered)\n", "\n", "\n", "def another_tokenize(example):\n", " question = example[\"questionText\"]\n", " answer = example[\"answerText\"]\n", " contents = [prompt, question, answer]\n", " message = [{\"role\": template[\"role\"], \"content\": template[\"content\"].format(content)} for template, content in zip(message_template, contents)]\n", " message = tokenizer.apply_chat_template(message, return_tensors=\"pt\")\n", " return {\"text\":message[0]}\n", "\n", "\n", "tokenized_dataset_2 = dataset_2.map(another_tokenize)\n", "print(tokenizer.decode(tokenized_dataset_2[\"text\"][0]))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 208, "referenced_widgets": [ "f5f0bc9dbac945259bee9759e04b35e3", "5157de627e5a4a14b418cc4466f542e7", "6861b3afb84b49aa926c7f6b7581a7d5", "7459d383d0be40b8ae54366fc01fe262", "4d3f40923b7842b69241e78252d9f316", "f502f32da73f4695b2f3b6669c842a25", "b440dfd42cbf42728e5a5b45b5696649", "9aaf424c529d4b1893c9f9f1ee01d1c1", "c7d9b66443b44315805f1ca83cc2d5ef", "ea64e51f23684288b59986e48c97e0e1", "205ee37ba26243cd89238ef1ffe86155" ] }, "id": "GYRr-BFDpFHq", "outputId": "f9d82525-3d5e-4726-8261-f87dc604fd51" }, "execution_count": 5, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/940 [00:00<|start_header_id|>system<|end_header_id|>\n", "\n", "You are a therapy chatbot, designed to offer emotional support and companionship to users seeking a listening ear. Your purpose is to engage in conversations that provide comfort, offer insights based on therapeutic principles, and suggest resources when appropriate. You need to act in a friendly and empathetic manner, ensuring that users feel heard and supported during their interactions with you.<|eot_id|><|start_header_id|>user<|end_header_id|>\n", "\n", "I have so many issues to address. I have a history of sexual abuse, I’m a breast cancer survivor and I am a lifetime insomniac. I have a long history of depression and I’m beginning to have anxiety. I have low self esteem but I’ve been happily married for almost 35 years.\n", " I’ve never had counseling about any of this. Do I have too many issues to address in counseling?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n", "\n", "Absolutely not.  I strongly recommending working on one issue/need at a time.  In therapy you will set smart goals and objectives that will help you reach your goals.  I see you as a survivor and not a victim.  Best wishes to you.<|eot_id|>\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Setting Up LoRA" ], "metadata": { "id": "KsyFdkN7CL62" } }, { "cell_type": "code", "source": [ "import torch\n", "from peft import LoraConfig, get_peft_model\n", "\n", "\n", "model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float32, device_map=\"auto\")\n", "\n", "# Freeze the model\n", "for param in model.parameters():\n", " param.requires_grad = False\n", "\n", "model.gradient_checkpointing_enable()\n", "model.enable_input_require_grads()\n", "\n", "# LORA adapters\n", "config = LoraConfig(\n", " r=16, # LoRA rank\n", " lora_alpha=32, # LoRA scaling\n", " lora_dropout=0.05,\n", " bias=\"none\", # are there biases of layers that you want to train?\n", " modules_to_save=None, # layers to unfreeze and train from the original pre-trained model\n", " task_type=\"CAUSAL_LM\")\n", "\n", "model = get_peft_model(model, config)\n", "model.print_trainable_parameters()" ], "metadata": { "id": "EhXuItTSCNUF", "colab": { "base_uri": "https://localhost:8080/", "height": 66, "referenced_widgets": [ "e11abf78d2184d928279fc7de61b208e", "03922a0bd00a477eb1db5a24b8fab1cf", "75eb0918e2b84b56b616d4e0b7747410", "c2ab7410b95b493abf2b75ba998f4e35", "4d8c909004c5433498bcc550a5f875ce", "1bbb2a17bd174022a00e6e4110fa582f", "ba05ad21a0e7479aafa75021abb45c64", "7bd2adade45a46cf9f17e423046df30a", "d41ebf9b810b457bbc97060205ecb882", "d5811a07860c42eaa5bf5bf4f4fb5921", "f6cfd7a64e29497c88715eb8e9ca2ce6" ] }, "outputId": "8bbe9bc7-9228-453e-a3d4-5fff86cf5d32" }, "execution_count": 6, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Loading checkpoint shards: 0%| | 0/4 [00:00" ], "text/html": [ "\n", "
\n", " \n", " \n", " [129/129 01:23, Epoch 3/3]\n", "
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StepTraining Loss
12.035200
22.440300
31.987100
42.004400
52.109200
61.612300
71.610300
82.008700
91.333500
101.556800
111.365500
121.463800
131.172800
141.170800
151.415200
161.206500
171.114500
181.058100
191.007300
201.226100
210.810900
221.011300
231.131600
240.953600
250.862700
260.854000
271.255600
280.990600
291.103300
301.091000
311.018300
320.840600
331.081000
341.113600
351.003300
361.325900
370.866900
380.912600
391.007300
400.761800
411.147900
420.762900
430.962800
441.122300
450.941600
460.985300
470.903500
480.889100
490.983100
500.814300
511.043200
520.753100
530.761000
540.817300
551.039500
560.811700
570.842200
580.892900
590.863500
600.874400
610.670500
621.125400
631.007000
640.959700
650.860100
660.868600
670.687900
680.855600
690.996800
701.227800
710.788800
721.131100
730.939900
740.848600
751.160700
760.847100
770.987500
780.857900
790.818400
800.981400
811.127600
820.990600
830.886300
840.772400
851.013000
861.049300
871.035500
880.812300
890.888700
900.808700
911.126400
920.720200
930.835700
940.985800
950.938100
960.824300
970.872600
981.139100
990.944100
1000.819500
1010.664200
1020.694100
1030.850700
1040.677200
1051.015500
1060.979900
1070.680900
1080.778400
1090.862600
1100.802300
1110.677100
1120.982300
1131.114900
1140.908700
1150.741500
1160.653900
1170.755000
1181.240100
1190.914800
1200.885100
1210.847100
1220.726500
1230.991600
1240.718400
1250.754300
1260.768900
1270.882800
1280.836900
1291.157700

" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "# Model Training on Dataset 2" ], "metadata": { "id": "veNHf3ltLuRM" } }, { "cell_type": "code", "source": [ "# Define trainer arguments\n", "trainer_2_args = TrainingArguments(\n", " per_device_train_batch_size=2,\n", " gradient_accumulation_steps=2,\n", " num_train_epochs=3,\n", " learning_rate=2e-4,\n", " warmup_ratio=0.03,\n", " fp16=True,\n", " logging_steps=5,\n", " output_dir=\"outputs\")\n", "\n", "\n", "# Define trainer\n", "trainer_2 = Trainer(\n", " model=model,\n", " args=trainer_2_args,\n", " train_dataset=tokenized_dataset_2[\"text\"],\n", " data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False)\n", ")\n", "\n", "\n", "# Train model\n", "model.config.use_cache = False # Supress Warnings, re-enable for inference later\n", "trainer_2.train()\n", "\n", "\n", "# Save the fine-tuned model\n", "trainer_2.save_model(\"finetuned_model_2\")" ], "metadata": { "id": "w6OeNB7Vzf70", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "12609eb1-af81-40a4-ad68-32fe0749d2ac" }, "execution_count": 10, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

\n", " \n", " \n", " [705/705 09:12, Epoch 3/3]\n", "
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StepTraining Loss
51.937900
101.928900
151.850000
201.922100
251.673800
301.782600
351.923600
401.805500
451.884300
501.886500
551.782400
601.816200
651.919600
701.710600
751.896300
801.755300
851.819700
901.793800
951.757700
1001.777400
1051.692000
1101.876600
1151.770800
1201.846000
1251.906200
1301.767400
1351.749600
1401.797900
1451.762100
1501.796600
1551.796800
1601.762500
1651.832900
1701.823500
1751.885300
1801.826200
1851.799100
1901.739100
1951.867600
2001.809800
2051.800100
2101.798900
2151.835800
2201.751300
2251.710000
2301.881700
2351.793300
2401.806900
2451.770700
2501.796700
2551.769900
2601.784300
2651.811600
2701.732000
2751.666400
2801.677400
2851.820700
2901.659500
2951.667800
3001.765100
3051.719200
3101.828000
3151.805600
3201.781000
3251.662300
3301.742200
3351.714500
3401.693700
3451.608100
3501.780700
3551.694400
3601.559900
3651.641600
3701.655600
3751.719200
3801.747800
3851.653700
3901.739900
3951.651900
4001.826100
4051.788700
4101.623900
4151.672400
4201.672100
4251.791100
4301.687000
4351.698900
4401.616600
4451.539200
4501.643000
4551.748800
4601.870800
4651.726900
4701.741500
4751.761000
4801.647400
4851.606400
4901.589900
4951.634000
5001.655500
5051.813400
5101.580000
5151.584000
5201.540400
5251.585400
5301.706400
5351.712000
5401.627300
5451.625000
5501.693900
5551.672300
5601.662200
5651.644700
5701.647400
5751.651400
5801.624000
5851.666000
5901.493200
5951.655900
6001.695700
6051.711100
6101.691600
6151.628000
6201.612300
6251.544400
6301.629700
6351.757900
6401.642900
6451.578700
6501.623900
6551.693600
6601.648000
6651.645900
6701.769000
6751.613400
6801.569900
6851.792000
6901.600800
6951.557700
7001.594300
7051.680800

" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "# Upload To HuggingFace Hub" ], "metadata": { "id": "ANKGOAzX7adc" } }, { "cell_type": "code", "source": [ "model.push_to_hub(\"John4Blues/Llama-3-8B-Therapy\", use_auth_token=True, commit_message=\"Just A Basic Trained Model\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 84, "referenced_widgets": [ "1a741f5ef12546a693be8960e60674cb", "32416b54fe03455bb8312d9885665a17", "5920aec36c114a78ba1ec41c8755fa2b", "c8c21ce1b1364a9f87be2cb78d428ecf", "ca5130da04d24ed4ae95fe18158a5a62", "9d18488e5bae46e29f73dd4d7fdddcf2", "9d073b0f97094a1f93ea891814da353b", "2a1f7704665b4c2a84ca643e940f2ae2", "2a5a98922beb47abaca3274f0250aadd", "c8996659a61c45c389ba6d251276d61e", "654206dbf9f14fc294f51ddd5c2b7cf6" ] }, "id": "6g9s3XK97xtY", "outputId": "0bff6491-e420-4491-93f1-6edaf2bac8a0" }, "execution_count": 12, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "adapter_model.safetensors: 0%| | 0.00/27.3M [00:00