{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "tjuxDHHYpFRi" }, "outputs": [], "source": [ "import os\n", "\n", "os.environ['KAGGLE_CONFIG_DIR'] = os.path.join(os.getcwd(), '.kaggle')\n", "os.environ[\"WANDB_DISABLED\"] = \"true\"\n", "os.environ[\"WANDB_DISABLED\"] = \"true\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "vNopCddhpIT9", "outputId": "d35ef347-e0dd-4e6c-986b-d243ac60659c" }, "outputs": [], "source": [ "!kaggle datasets download -d xmaulana/psychikadataset-7b" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "v55BKTnus50u" }, "outputs": [], "source": [ "import zipfile\n", "with zipfile.ZipFile('psychikadataset-7b.zip', 'r') as zip_ref:\n", " zip_ref.extractall('psychikadataset-7b')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "wldDKSh2pJmM", "outputId": "02db19d7-74d0-4787-fc2b-8af3f084e6cf" }, "outputs": [], "source": [ "from trl import SFTTrainer\n", "from transformers import TrainingArguments, AutoTokenizer, AutoModelForCausalLM, DataCollatorForLanguageModeling\n", "from datasets import Dataset\n", "from peft import LoraConfig, get_peft_model, TaskType\n", "import pandas as pd\n", "import torch" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 693, "referenced_widgets": [ "53cf5b03a88442368670bd1b5f2b64df", "6a635fef10314457827ef62bfc2520da", "082b4e58b8054b6b8aec8884a76006ce", "b90080a74ecf4553876bedddc0f5087f", "726e692c5cba48249e5e2616b95bbaed", "1bd38dfbbd614cb7bf5ab3c060192156", "2b61f910ea544680a223e77a70699c6e", "9647555f83d54c4e9fed45bec2ab153a", "729d5b9900a24414afa510c0d69907fc", "b491b011a88a41f297af2c3caddb0fc8", "76869b1f95a7425286fad301c370c184", "d2177958cea94ff4b275d16d14b2c003", "1b03df63749e4fbea7cd4df3833155c4", 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"83bf0523bcf94542837009b03092b35d", "f20f071ae7874105a82e22102254cf37", "cf9d27e30a1347e59eb7e8db255347ce", "6dd2307b7b5d4e838c950d856b7d875c", "06134b22ff4846c98ee8ddf51998b006" ] }, "id": "UYvOWjL0pKJq", "outputId": "3152e121-bda8-462d-8044-6d78a6e6e51c" }, "outputs": [], "source": [ "# Cek device\n", "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "print(\"Device:\", device)\n", "\n", "# Load model dan tokenizer\n", "model_name = \"Qwen/Qwen1.5-1.8B-Chat\"\n", "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n", "model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)\n", "\n", "# Konfigurasi Scaled Dot-Product Attention jika diperlukan (optional)\n", "torch.backends.cuda.enable_flash_sdp(False)\n", "torch.backends.cuda.enable_math_sdp(True)\n", "torch.backends.cuda.enable_mem_efficient_sdp(False)\n", "\n", "# Aktifkan gradient checkpointing\n", "model.gradient_checkpointing_enable()\n", "model.config.use_cache = False\n", "\n", "# LoRA config\n", "peft_config = LoraConfig(\n", " task_type=TaskType.CAUSAL_LM,\n", " r=8,\n", " lora_alpha=32,\n", " lora_dropout=0.05,\n", " bias=\"none\",\n", " target_modules=[\"c_attn\", \"q_proj\", \"v_proj\"], # sesuaikan jika error\n", ")\n", "\n", "# Terapkan LoRA ke model\n", "model = get_peft_model(model, peft_config)\n", "model.to(device)\n", "\n", "# Load dataset\n", "df = pd.read_csv(\"psychikadataset-7b/data.csv\")\n", "\n", "# Format sesuai template chat Qwen\n", "def format_chat_template(row):\n", " chat = [\n", " {\"role\": \"user\", \"content\": row[\"Context\"]},\n", " {\"role\": \"assistant\", \"content\": row[\"Response\"]}\n", " ]\n", " return tokenizer.apply_chat_template(chat, tokenize=False)\n", "\n", "df[\"text\"] = df.apply(format_chat_template, axis=1)\n", "\n", "# Convert ke HuggingFace Dataset\n", "dataset = Dataset.from_pandas(df[[\"text\"]])\n", "\n", "# Tokenisasi\n", "def tokenize(sample):\n", " return tokenizer(sample[\"text\"], truncation=True, padding=\"max_length\", max_length=512)\n", "\n", "tokenized_dataset = dataset.map(tokenize, batched=True)\n", "\n", "# Data collator\n", "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)\n", "\n", "# TrainingArguments\n", "training_args = TrainingArguments(\n", " output_dir=\"./qwen-psychika-lora\",\n", " per_device_train_batch_size=1,\n", " num_train_epochs=1,\n", " learning_rate=5e-5,\n", " logging_steps=99999999,\n", " save_steps=100,\n", " save_total_limit=1,\n", " fp16=True, # GUNAKAN fp16 untuk T4\n", " report_to=[],\n", " logging_strategy=\"no\"\n", ")\n", "\n", "# Trainer\n", "trainer = SFTTrainer(\n", " model=model,\n", " train_dataset=tokenized_dataset,\n", " args=training_args,\n", " data_collator=data_collator,\n", ")\n", "\n", "# Mulai training\n", "trainer.train()\n", "\n", "# Simpan model dan tokenizer\n", "model.save_pretrained(\"qwen-psychika-lora\")\n", "tokenizer.save_pretrained(\"qwen-psychika-lora\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "5aChAFj_vA3Z", "outputId": "02a04f78-44e9-47df-c42c-d5274e657a7c" }, "outputs": [], "source": [ "!zip -r qwen-psychika-lora.zip qwen-psychika-lora" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "8IZu4pHXviEk", "outputId": "93b4bbbd-0aa0-4b15-dc9f-58a08f4a75e8" }, "outputs": [], "source": [ "from transformers import AutoTokenizer, AutoModelForCausalLM\n", "from peft import PeftModel\n", "import torch\n", "\n", "device = \"cpu\"\n", "base_model_path = \"./qwen-1.5-1.8B-local\" # folder lokal\n", "adapter_path = \"./qwen-psychika-lora\" # LoRA folder lokal\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(base_model_path, trust_remote_code=True)\n", "base_model = AutoModelForCausalLM.from_pretrained(base_model_path, trust_remote_code=True)\n", "\n", "model = PeftModel.from_pretrained(base_model, adapter_path).to(device)\n", "model.eval()\n", "\n", "# Inisialisasi chat history\n", "chat_history = [\n", " {\"role\": \"system\", \"content\": \"Kamu adalah seorang profesional psikiater yang sedang memeriksa pasien yang sedang mengalami tekanan mental.\"}\n", "]\n", "\n", "print(\"Mulai percakapan dengan Qwen Psychika Chat (ketik 'exit' untuk keluar):\")\n", "\n", "# Loop percakapan\n", "while True:\n", " user_input = input(\"You: \")\n", " if user_input.lower() == \"exit\":\n", " break\n", "\n", " chat_history.append({\"role\": \"user\", \"content\": user_input})\n", "\n", " # Format prompt\n", " chat_prompt = tokenizer.apply_chat_template(chat_history, tokenize=False)\n", "\n", " # Tokenisasi dan pindahkan ke device\n", " inputs = tokenizer(chat_prompt, return_tensors=\"pt\").to(model.device)\n", "\n", " with torch.no_grad():\n", " outputs = model.generate(\n", " **inputs,\n", " max_new_tokens=256,\n", " do_sample=True,\n", " temperature=0.7,\n", " top_p=0.9,\n", " pad_token_id=tokenizer.eos_token_id\n", " )\n", "\n", " # Ambil token output baru saja (tanpa prompt)\n", " prompt_len = inputs[\"input_ids\"].shape[1]\n", " generated_tokens = outputs[0][prompt_len:]\n", " reply = tokenizer.decode(generated_tokens, skip_special_tokens=True).strip()\n", "\n", " print(\"AI:\", reply)\n", "\n", " chat_history.append({\"role\": \"assistant\", \"content\": reply})" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.11" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "01b889c79db24224891a2498c95af16f": { "model_module": "@jupyter-widgets/controls", 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