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"id": "xtICe_5w79Oe", "outputId": "90289769-0df6-415f-a3d1-d4fc99197004" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " 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", " 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", " 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" ] } ] }, { "cell_type": "code", "source": [ "from datasets import load_dataset\n", "\n", "dataset = load_dataset(\"Devdeshitha/testdataset1\")\n" ], "metadata": { "id": "xLaZg8TS7bgw" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from datasets import load_dataset\n", "\n", "train_dataset = load_dataset('json', data_files='notes.jsonl', split='train')\n", "eval_dataset = load_dataset('json', data_files='notes_validation.jsonl', split='train')" ], "metadata": { "id": "A3zGMMinMs75" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "def formatting_func(example):\n", " text = f\"### Question: {example['input']}\\n ### Answer: {example['output']}\"\n", " return text" ], "metadata": { "id": "QPaDjNP8OgQ2" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "import torch\n", "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n", "\n", "base_model_id = \"mistralai/Mistral-7B-v0.1\"\n", "bnb_config = BitsAndBytesConfig(\n", " load_in_4bit=True,\n", " bnb_4bit_use_double_quant=True,\n", " bnb_4bit_quant_type=\"nf4\",\n", " bnb_4bit_compute_dtype=torch.bfloat16\n", ")\n", "\n", "model = AutoModelForCausalLM.from_pretrained(base_model_id, quantization_config=bnb_config)" ], "metadata": { "id": "P1W3btN1-fos" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "tokenizer = AutoTokenizer.from_pretrained(\n", " base_model_id,\n", " padding_side=\"left\",\n", " add_eos_token=True,\n", " add_bos_token=True,\n", ")\n", "tokenizer.pad_token = tokenizer.eos_token\n", "\n", "def generate_and_tokenize_prompt(prompt):\n", " return tokenizer(formatting_func(prompt))" ], "metadata": { "id": "uDe0t4ghOoKk", "colab": { "base_uri": "https://localhost:8080/", "height": 17, "referenced_widgets": [ "83c67e9efb87414983dc54e57e0c4b13", "f0162e38dc704406b147b31b9f1af532", "68bd583e714f493f8813c4bbe23faaef", "cf14339119954f3e9dd5954e4560f4b2", "35ae0502bab148c596a841a2b684b542", "27a527b4f7f94b7b8f8387eea24799b9", "7a7286d9c0384f2c965ba484596b463e", "d1118f0fe0eb4ec4b3461a407d06222a", 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