zero_shot.ipynb

{"cells":[{"cell_type":"code","execution_count":7,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T21:05:52.957888Z","iopub.status.busy":"2024-04-01T21:05:52.957058Z","iopub.status.idle":"2024-04-01T21:05:52.963284Z","shell.execute_reply":"2024-04-01T21:05:52.962314Z","shell.execute_reply.started":"2024-04-01T21:05:52.957853Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["/kaggle/working\n"]}],"source":["%cd /kaggle/working"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":["import matplotlib.pyplot as plt\n","from dataclasses import dataclass\n","from transformers import AutoModelForCausalLM, AutoTokenizer\n","import seaborn as sns\n","from datetime import datetime\n","import pandas as pd\n","import numpy as np\n","import os\n","import torch\n","from sklearn.metrics import mean_absolute_error, mean_squared_error\n","from sklearn.neighbors import KernelDensity\n","import warnings\n","\n","warnings.filterwarnings(\"ignore\")\n","\n","os.environ[\"HUGGINGFACEHUB_API_TOKEN\"] = ''"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":["!huggingface-cli login --token ''"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":["!pip install transformers bitsandbytes>=0.39.0 -q\n","!pip install accelerate"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":["@dataclass\n","class Scaler:\n","    transform: callable = lambda x: x\n","    inv_transform: callable = lambda x: x"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":["model_cache = {}"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":["class LLMTime:\n","\n","    def __init__(self, train, test, alpha=0.95, beta=0.3, batch_length=400, basic=False, temperature=0.5, do_sample=True, model_name=\"mistralai/Mistral-7B-v0.1\", repetition_penalty=1.0, all_in_one=False, pretrain=False, load_in_4bit=True, num_samples=10, k=5):\n","        self.model_name = model_name\n","        self.train = train\n","        self.test = test\n","        self.alpha = alpha\n","        self.beta = beta\n","        self.batch_length = batch_length\n","        self.basic = basic\n","        self.temperature = temperature\n","        self.do_sample = do_sample\n","        self.repetition_penalty = repetition_penalty\n","        self.scalers = None\n","        self.input_str = None\n","        self.test_str = None\n","        self.tokenizer = None\n","        self.model = None\n","        self.good_tokens = None\n","        self.bad_tokens = None\n","        self.output = None\n","        self.transformed_output_arr = None\n","        self.all_in_one = all_in_one\n","        self.pretrain = pretrain\n","        self.load_in_4bit = load_in_4bit\n","        self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","        self.num_samples = num_samples\n","        self.preprocess_data()\n","        self.k = 5\n","\n","    @staticmethod\n","    def get_scaler(history, alpha=0.95, beta=0.3, basic=False):\n","        history = history[~np.isnan(history)]\n","        if basic:\n","            q = np.maximum(np.quantile(np.abs(history), alpha),.01)\n","            def transform(x):\n","                return x / q\n","            def inv_transform(x):\n","                return x * q\n","        else:\n","            min_ = np.min(history) - beta*(np.max(history)-np.min(history))\n","            q = np.quantile(history-min_, alpha)\n","            if q == 0:\n","                q = 1\n","            def transform(x):\n","                return (x - min_) / q\n","            def inv_transform(x):\n","                return x * q + min_\n","        return Scaler(transform=transform, inv_transform=inv_transform)\n","\n","    def convert_array_to_string(self, arr):\n","        rounded_values = [round(val * 1000) for val in arr]\n","        str_values = [str(val) for val in rounded_values]\n","        result_string = \",\".join(str_values)\n","        return result_string\n","\n","    def preprocess_data(self):\n","        train = self.train\n","        test = self.test\n","        if not isinstance(train, list):\n","            train = [train]\n","            test = [test]\n","        n_val = len(train)\n","        for i in range(len(train)):\n","            if not isinstance(train[i], pd.Series):\n","                train[i] = pd.Series(train[i], index=pd.RangeIndex(len(train[i])))\n","                test[i] = pd.Series(test[i], index=pd.RangeIndex(len(train[i]), len(test[i])+len(train[i])))\n","        alpha = self.alpha\n","        beta = self.beta\n","        basic = self.basic\n","        self.scalers = [self.get_scaler(train[i].values, alpha=alpha, beta=beta, basic=basic) for i in range(len(train))]\n","        input_arrs = [train[i].values for i in range(len(train))]\n","        transformed_input_arrs = np.array([scaler.transform(input_array) for input_array, scaler in zip(input_arrs, self.scalers)])\n","        input_str = self.convert_array_to_string(transformed_input_arrs[0])\n","        test_arrs = [test[i].values for i in range(len(test))]\n","        transformed_test_arrs = np.array([scaler.transform(input_array) for input_array, scaler in zip(test_arrs, self.scalers)])\n","        test_str = self.convert_array_to_string(transformed_test_arrs[0])\n","        self.input_str = input_str\n","        self.test_str = test_str\n","        return input_str, test_str, self.scalers\n","\n","    def zero_shot(self):\n","        from transformers import BitsAndBytesConfig\n","        quantization_config = BitsAndBytesConfig(load_in_4bit=True) if self.load_in_4bit else None\n","        input_str = self.input_str\n","        if model_cache.get(self.model_name) is not None:\n","            self.model = model_cache[self.model_name][0]\n","            self.tokenizer = model_cache[self.model_name][1]\n","        else:\n","            self.model = AutoModelForCausalLM.from_pretrained(self.model_name, device_map='auto', quantization_config=quantization_config)\n","            self.tokenizer = AutoTokenizer.from_pretrained(self.model_name, padding_side=\"left\")\n","            model_cache[self.model_name] = [self.model, self.tokenizer]\n","        good_tokens_str = list(\"0123456789,\")\n","        good_tokens = [self.tokenizer.convert_tokens_to_ids(token) for token in good_tokens_str]\n","        bad_tokens = [i for i in range(len(self.tokenizer)) if i not in good_tokens]\n","        if self.pretrain:\n","            for i in range(self.batch_length, len(input_str) - self.batch_length//10, self.batch_length//10):\n","              print(i)  \n","              batch_str = input_str[i-self.batch_length: i]\n","              model_inputs = self.tokenizer(batch_str, return_tensors='pt').to(self.device)\n","              generated_ids = self.model.generate(**model_inputs, do_sample=True, max_new_tokens=self.batch_length//self.k, min_new_tokens=self.batch_length//self.k,temperature=self.temperature, repetition_penalty=self.repetition_penalty, bad_words_ids=[[t] for t in bad_tokens])\n","              output = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]\n","\n","        if self.all_in_one:\n","            batch_str = input_str[-self.batch_length:]\n","            model_inputs = self.tokenizer(batch_str, return_tensors='pt').to(self.device)\n","            generated_ids = self.model.generate(**model_inputs, do_sample=True, max_new_tokens=self.batch_length//self.k, min_new_tokens=self.batch_length//self.k,temperature=self.temperature, repetition_penalty=self.repetition_penalty, bad_words_ids=[[t] for t in bad_tokens])\n","            output = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]\n","            output = output[len(batch_str):]\n","            self.output = output\n","            return output\n","\n","        inp = input_str[-self.batch_length:]\n","        out = \"\"\n","        k = max(1, len(self.test_str)//(self.batch_length//self.k))\n","        for i in range(k):\n","            print(i)\n","            model_inputs = self.tokenizer(inp, return_tensors='pt').to(self.device)\n","            generated_ids = self.model.generate(**model_inputs, do_sample=True, max_new_tokens=self.batch_length//self.k, min_new_tokens=self.batch_length//self.k,temperature=self.temperature, repetition_penalty=self.repetition_penalty, bad_words_ids=[[t] for t in bad_tokens])\n","            output = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]\n","            output = output[len(inp):]\n","            out += output\n","            inp += output\n","            inp = inp[-self.batch_length:]\n","        self.output = out\n","        return out\n","\n","    @staticmethod\n","    def invert_string_to_array(string_values):\n","        string_values = string_values.replace(\" \", \"\")\n","        if string_values[-1] == ',':\n","            string_values = string_values[:len(string_values) - 1]\n","        str_values_list = string_values.split(',')\n","        float_values = [float(val) if val.strip() != '' else 0.0 for val in str_values_list]\n","        original_values = [val / 1000 for val in float_values]\n","        return original_values\n","\n","    def get_original_array(self, output_str):\n","        output_arr = self.invert_string_to_array(string_values=output_str)\n","        output_arr = np.array(output_arr)\n","        transformed_output_arr = self.scalers[0].inv_transform(output_arr)\n","        self.transformed_output_arr = transformed_output_arr\n","        return transformed_output_arr\n","\n","    def plot(self, input, outputs, m):\n","          model_parameters = {\n","              \"alpha\": self.alpha,\n","              \"beta\": self.beta,\n","              \"batch_length\": self.batch_length,\n","              \"basic\": self.basic,\n","              \"temperature\": self.temperature,\n","              \"do_sample\": self.do_sample,\n","              \"repetition_penalty\": self.repetition_penalty,\n","              \"all_in_one\": self.all_in_one,\n","              \"pretrain\": self.pretrain,\n","              \"load_in_4bit\": self.load_in_4bit,\n","              \"num_samples\": self.num_samples\n","          }\n","          k = min(len(input), len(outputs[0]))\n","          sns.set(style=\"whitegrid\")\n","          fig, ax = plt.subplots(figsize=(10, 6))\n","          plt.title(f\"Zero Shot {self.model_name}\")\n","          plt.axvline(x=m, color='k', linestyle='--', label='Train-Test Split')\n","          min_output = np.min(outputs, axis=0)[:k]\n","          max_output = np.max(outputs, axis=0)[:k]\n","          median_output = np.median(outputs, axis=0)[:k]\n","          mean_output = np.mean(outputs, axis=0)[:k]\n","          input = input[:k]\n","\n","\n","          for i in range(len(outputs)):\n","            plt.plot(outputs[i][:k], color='darkgray', alpha=0.5, label='_nolegend_')\n","\n","          plt.plot(min_output, color='green', linestyle='--', label='Min Predicted')\n","          plt.plot(max_output, color='orange', linestyle='--', label='Max Predicted')\n","          plt.plot(median_output, color='red', label='Median Predicted')\n","          plt.plot(mean_output, color='violet', label='Mean Predicted')\n","\n","          plt.plot(input, color='blue', label='Actual')\n","          plt.legend(loc='upper left')\n","\n","          plt.grid(True)\n","          plt.xlabel('Time')\n","          sns.set_palette(\"husl\")\n","          \n","          min_input = np.min(input)\n","          max_input = np.max(input)\n","          plt.ylim(int(0.99*min_input), int(1.1*max_input))\n","\n","          mae = mean_absolute_error(input[-(len(input)-m):], median_output[-(len(median_output)-m):])\n","          rmse = np.sqrt(mean_squared_error(input[-(len(input)-m):], median_output[-(len(median_output)-m):]))\n","          input_range = np.max(input) - np.min(input)\n","          input_std = np.std(input)\n","          scaled_mae = mae / input_range\n","          scaled_rmse = rmse / input_std\n","          std_dev = np.std(outputs, axis=0)\n","          error_text = f\"\\n\\nMedian MAE: {mae:.2f}\\nMedian RMSE: {rmse:.2f}\\nStandard Deviation: {np.mean(std_dev):.2f}\"\n","          ax.text(1.05, 0.4, error_text, transform=ax.transAxes, fontsize=10, verticalalignment='center')\n","          textstr = '\\n'.join([f\"{key}: {value}\" for key, value in model_parameters.items()])\n","          props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)\n","          ax.text(1.05, 0.6, textstr, transform=ax.transAxes, fontsize=10, verticalalignment='center', bbox=props)\n","\n","          model_name = self.model_name.split('/')[1]\n","          current_time = datetime.now().strftime(\"%Y-%m-%d_%H-%M-%S\")\n","          random_number = np.random.randint(0, 1000)\n","          filename = f\"{model_name}-zero-shot-{current_time}-{random_number}.png\"\n","\n","          plt.savefig(filename, dpi=300, bbox_inches='tight')\n","          print(f\"Plot saved as {filename}\")\n","\n","\n","    def run(self):\n","        outputs = []\n","        for i in range(self.num_samples):\n","            output = self.zero_shot()\n","            outputs.append(output)\n","        r = np.array(outputs)\n","        self.plot_and_return(outputs)\n","        return outputs\n","\n","    def plot_and_return(self, outputs):\n","      l = []\n","      for i, output in enumerate(outputs):\n","        firstCommaIndex = output.find(',')\n","        lastCommaIndex = output.rfind(',')\n","        outputs[i] = output[firstCommaIndex+1:lastCommaIndex]\n","      input_arr = self.get_original_array(self.input_str + ',' + self.test_str)\n","      inp = self.get_original_array(self.input_str)\n","      m = len(inp)\n","      min_len = 9999999999\n","      for i, output in enumerate(outputs):\n","        transformed_output_arr = self.get_original_array(self.input_str + ',' + output)\n","        min_len = min(min_len, len(transformed_output_arr))\n","        l.append(transformed_output_arr)\n","      input_arr = input_arr[:min_len]\n","      for i, x in enumerate(l):\n","        l[i] = x[:min_len]\n","      self.plot(input_arr, np.array(l), m)"]},{"cell_type":"code","execution_count":21,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T22:35:41.818676Z","iopub.status.busy":"2024-04-01T22:35:41.818005Z","iopub.status.idle":"2024-04-01T23:25:01.169508Z","shell.execute_reply":"2024-04-01T23:25:01.168488Z","shell.execute_reply.started":"2024-04-01T22:35:41.818644Z"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["0\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["1\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["2\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["3\n","4\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["5\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end 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generation.\n"]},{"name":"stdout","output_type":"stream","text":["4\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["5\n","6\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["7\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["8\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["9\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["10\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["0\n","1\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["2\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["3\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["4\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["5\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["6\n","7\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["8\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["9\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["10\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["0\n","1\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["2\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["3\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["4\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["5\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["6\n","7\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["8\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["9\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["10\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["0\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["1\n","2\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["3\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["4\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["5\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["6\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["7\n","8\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["9\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["10\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["0\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["1\n","2\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["3\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["4\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["5\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["6\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["7\n","8\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["9\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["10\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["0\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["1\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["2\n","3\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["4\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["5\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["6\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["7\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["8\n","9\n"]},{"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n","Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"]},{"name":"stdout","output_type":"stream","text":["10\n","Plot saved as Mistral-7B-v0.1-zero-shot-2024-04-01_23-24-59-735.png\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["llm = LLMTime(train,\n","              test,\n","              alpha=0.95,\n","              beta=0.3,\n","              batch_length=2000,\n","              basic=False,\n","              temperature=0.5,\n","              do_sample=True,\n","              repetition_penalty=1.3,\n","              all_in_one=False,\n","              pretrain=False,\n","              load_in_4bit=True,\n","              num_samples=10)\n","out = llm.run()"]}],"metadata":{"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"datasetId":1057,"sourceId":1913,"sourceType":"datasetVersion"}],"dockerImageVersionId":30674,"isGpuEnabled":true,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","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":"3.10.13"}},"nbformat":4,"nbformat_minor":4}