diff --git "a/model_train.ipynb" "b/model_train.ipynb"
--- "a/model_train.ipynb"
+++ "b/model_train.ipynb"
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 57,
    "id": "d8221be3",
    "metadata": {
     "id": "d8221be3"
@@ -13,11 +13,19 @@
     "import numpy as np\n",
     "import json\n",
     "import transformers\n",
+    "import matplotlib.pyplot as plt\n",
+    "import IPython\n",
+    "\n",
+    "from math import ceil\n",
+    "\n",
+    "from IPython.display import clear_output\n",
     "from tqdm.auto import tqdm\n",
     "\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
     "from torch.utils.data import Dataset, DataLoader\n",
     "\n",
-    "from transformers import AutoTokenizer, AutoModel, pipeline, DistilBertForSequenceClassification\n",
+    "from transformers import AutoTokenizer, AutoModel, pipeline, DistilBertForSequenceClassification, DistilBertConfig\n",
     "from sklearn.model_selection import train_test_split"
    ]
   },
@@ -58,7 +66,7 @@
    "outputs": [],
    "source": [
     "import json\n",
-    "file = open('./data/2/arxivData.json')\n",
+    "file = open('/home/mosievich.kirill/ysda/ml_3/data/2/arxivData.json')\n",
     "data = json.load(file)"
    ]
   },
@@ -131,7 +139,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2632dead9c7c41389f73164b6e84e6f9",
+       "model_id": "55b292bdfc3f44cc9a0bf0d49c1825c4",
        "version_major": 2,
        "version_minor": 0
       },
@@ -145,7 +153,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3c56aecc808646b9a1901c6a9e29271b",
+       "model_id": "d50b32f79b9c4a8f9c645f67e5bbb95f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -159,7 +167,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "30a22e4b17a44919823f2bbba06434ef",
+       "model_id": "8074ff0f809c45d4936328aeacff76b6",
        "version_major": 2,
        "version_minor": 0
       },
@@ -356,150 +364,12 @@
     "    tags[i] = int_line"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "ea5b030c",
-   "metadata": {
-    "id": "ea5b030c"
-   },
-   "outputs": [],
-   "source": [
-    "#data = pd.read_json(\"arxivData.json\")\n",
-    "#data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "207f9232",
-   "metadata": {
-    "id": "207f9232"
-   },
-   "outputs": [],
-   "source": [
-    "#data[[\"tag\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "a3c87ee3",
-   "metadata": {
-    "id": "a3c87ee3"
-   },
-   "outputs": [],
-   "source": [
-    "#len(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "04a97667",
-   "metadata": {
-    "id": "04a97667"
-   },
-   "outputs": [],
-   "source": [
-    "#x = data[[\"summary\", \"title\"]]\n",
-    "#(np.array(x)).shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "b35c8f07",
-   "metadata": {
-    "id": "b35c8f07"
-   },
-   "outputs": [],
-   "source": [
-    "#x = x[\"summary\"].values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "037f7469",
-   "metadata": {
-    "id": "037f7469"
-   },
-   "outputs": [],
-   "source": [
-    "#x = list(x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "7e78c99d",
-   "metadata": {
-    "id": "7e78c99d"
-   },
-   "outputs": [],
-   "source": [
-    "#data[\"tag\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "45c6dc0f",
-   "metadata": {
-    "id": "45c6dc0f"
-   },
-   "outputs": [],
-   "source": [
-    "#import ast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "407caa74",
-   "metadata": {
-    "id": "407caa74"
-   },
-   "outputs": [],
-   "source": [
-    "#tags = (data[\"tag\"]).apply(ast.literal_eval)\n",
-    "\n",
-    "#y = [[tags[row][tag_index]['term'] for tag_index in range(len(tags[row]))] for row in range(len(tags))]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "2fdeb510",
-   "metadata": {
-    "id": "2fdeb510"
-   },
-   "outputs": [],
-   "source": [
-    "#[len(y[row]) for row in range(len(y))]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "25a0b554",
-   "metadata": {
-    "id": "25a0b554"
-   },
-   "outputs": [],
-   "source": [
-    "## кол-во различных тем\n",
-    "#kkk_ans = []\n",
-    "#dd = [[kkk_ans.append(prepared(tags[row][tag_index]['term'])) for tag_index in range(len(tags[row]))] for row in range(len(tags))]\n",
-    "#set(kkk_ans)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "1eace08b",
    "metadata": {
-    "id": "1eace08b"
+    "id": "1eace08b",
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "## Разделение данных на train test и подготовка батчей"
@@ -507,7 +377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 28,
    "id": "1aaae017",
    "metadata": {
     "id": "1aaae017"
@@ -519,7 +389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 29,
    "id": "2ea8d260",
    "metadata": {
     "id": "2ea8d260"
@@ -533,7 +403,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 30,
    "id": "cf736b38",
    "metadata": {
     "id": "cf736b38"
@@ -546,7 +416,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 31,
    "id": "065yUVS355c8",
    "metadata": {
     "id": "065yUVS355c8"
@@ -575,7 +445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 32,
    "id": "SYQAUdgQ6ENy",
    "metadata": {
     "id": "SYQAUdgQ6ENy"
@@ -599,7 +469,8 @@
    "cell_type": "markdown",
    "id": "52f5d981",
    "metadata": {
-    "id": "52f5d981"
+    "id": "52f5d981",
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "## Токенизация"
@@ -607,7 +478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 33,
    "id": "42582978",
    "metadata": {
     "id": "42582978"
@@ -619,7 +490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 34,
    "id": "5b3986b0",
    "metadata": {
     "id": "5b3986b0"
@@ -631,7 +502,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 35,
    "id": "1086f2dd",
    "metadata": {
     "id": "1086f2dd"
@@ -647,7 +518,8 @@
    "cell_type": "markdown",
    "id": "ec13f592",
    "metadata": {
-    "id": "ec13f592"
+    "id": "ec13f592",
+    "jp-MarkdownHeadingCollapsed": true
    },
    "source": [
     "## Модель классификации"
@@ -655,7 +527,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 36,
    "id": "31cb6543-6870-4103-be35-5861c39f0e5e",
    "metadata": {},
    "outputs": [
@@ -706,7 +578,7 @@
        ")"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -721,7 +593,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 37,
    "id": "f5271f2d",
    "metadata": {
     "id": "f5271f2d"
@@ -733,7 +605,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 38,
    "id": "f8e34c00",
    "metadata": {
     "id": "f8e34c00"
@@ -774,7 +646,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 39,
    "id": "d133a5cc",
    "metadata": {
     "id": "d133a5cc"
@@ -803,44 +675,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 40,
    "id": "iGEpBdTkhJQm",
    "metadata": {
     "id": "iGEpBdTkhJQm"
    },
    "outputs": [],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
     "def plot_learning_process(train_loss, val_loss):    \n",
     "    plt.figure()\n",
-    "    plt.title('loss by epoch')\n",
-    "    plt.plot(np.arange(0, len(val_loss))+0.5, train_loss, label='train')\n",
-    "    plt.plot(np.arange(0, len(val_loss))+1, val_loss, label='val')\n",
+    "    plt.plot(np.arange(0, len(val_loss)) + 0.5, train_loss, label='train')\n",
+    "    plt.plot(np.arange(0, len(val_loss)) + 1, val_loss, label='val')\n",
     "    plt.legend()\n",
-    "    plt.grid()\n",
     "    plt.xlabel('epoch')\n",
-    "    plt.ylabel('loss function')\n",
+    "    plt.ylabel('Loss')\n",
     "    plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 43,
    "id": "d09fb6df",
    "metadata": {
     "id": "d09fb6df"
    },
    "outputs": [],
    "source": [
-    "device = 'cuda'\n",
-    "\n",
-    "import IPython\n",
-    "from math import ceil\n",
-    "\n",
-    "\n",
     "def train_loop(model, dataloader, loss_fn, optimizer, step=0.05, history_loss=None):\n",
-    "    out = display(IPython.display.Pretty('Learning...'), display_id=True)\n",
+    "    out_display = display(IPython.display.Pretty('Traininf'), display_id=True)\n",
     "    \n",
     "    size = len(dataloader.dataset) \n",
     "    len_size = len(str(size))\n",
@@ -848,20 +710,19 @@
     "        \n",
     "    train_loss = []\n",
     "    percentage = 0\n",
-    "    for batch, (X, y) in enumerate(tqdm(dataloader, leave=False, desc=\"Batch #\")):\n",
+    "    for batch, (X, y) in enumerate(tqdm(dataloader, leave=False, desc=\"Batch: \")):\n",
     "        X, y = X.to(device), y.to(device)\n",
-    "        # evaluate\n",
+    "\n",
     "        output = F.softmax(model(X).logits, dim=1)\n",
     "        loss = loss_fn(output, y)\n",
     "        train_loss.append(loss.item())\n",
     "        \n",
-    "        # backpropagation\n",
     "        optimizer.zero_grad()\n",
     "        loss.backward()\n",
     "        optimizer.step()\n",
     "        # print info\n",
     "        if batch / batches > percentage or batch == batches: \n",
-    "            out.update(f'[{int(percentage * size)}/{size}] Loss: {train_loss[-1]:>8f}')\n",
+    "            out_display.update(f'Steps: {int(percentage * size)} / {size} | Loss: {train_loss[-1]}')\n",
     "            percentage += step\n",
     "        \n",
     "    if history_loss is not None:\n",
@@ -878,8 +739,7 @@
     "    val_loss = []\n",
     "    \n",
     "    with torch.no_grad():\n",
-    "        # evalute and check predictions\n",
-    "        for batch, (X, y) in enumerate(tqdm(dataloader, leave=False, desc='Batch #')):\n",
+    "        for batch, (X, y) in enumerate(tqdm(dataloader, leave=False, desc='Batch: ')):\n",
     "            X, y = X.to(device), y.to(device)\n",
     "            output = F.softmax(model(X).logits, dim=1)\n",
     "            loss = loss_fn(output, y)\n",
@@ -890,7 +750,7 @@
     "    test_loss /= batches\n",
     "    correct /= size\n",
     "    \n",
-    "    print(f\"Validation accuracy: {(100*correct):>0.1f}%, Validation loss: {test_loss:>8f} \\n\")\n",
+    "    print(f\"Validation accuracy: {100 * correct}, Validation loss: {test_loss} \\n\")\n",
     "    \n",
     "    if history_loss is not None:\n",
     "        history_loss.append(np.mean(val_loss))\n",
@@ -900,17 +760,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "53b96267",
-   "metadata": {
-    "id": "53b96267"
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "id": "8b3fdae8",
    "metadata": {
     "colab": {
@@ -1057,51 +907,17 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 6\n",
-      "-------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'[22140/36900] Loss: 0.016933'"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "42b373e48394437aa48a75cf3fce74a7",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Batch #:   0%|          | 0/289 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "import torch.nn as nn\n",
-    "import torch.nn.functional as F\n",
-    "\n",
-    "from IPython.display import clear_output\n",
+    "device = 'cuda'\n",
     "\n",
     "\n",
     "loss_fn = F.binary_cross_entropy\n",
@@ -1112,7 +928,7 @@
     "val_loss = []\n",
     "    \n",
     "for epoch in range(epochs):\n",
-    "    print(f\"Epoch {epoch+1}\\n-------------------------------\")\n",
+    "    print(f\"Epoch: {epoch + 1}\")\n",
     "    \n",
     "    train_loop(classifier, base_train_dl, loss_fn, optimizer, history_loss=train_loss)\n",
     "    test_loop(classifier, base_test_dl, loss_fn, history_loss=val_loss)\n",
@@ -1123,39 +939,121 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 45,
+   "id": "81d6a077-eaf4-4729-9843-f2955529357b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for epoch in range(2):\n",
+    "    print(f\"Epoch: {epoch + 1}\")\n",
+    "    \n",
+    "    train_loop(classifier, base_train_dl, loss_fn, optimizer, history_loss=train_loss)\n",
+    "    test_loop(classifier, base_test_dl, loss_fn, history_loss=val_loss)\n",
+    "    \n",
+    "    clear_output()\n",
+    "    plot_learning_process(train_loss, val_loss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
    "id": "pJMF9lg16z2U",
    "metadata": {
     "id": "pJMF9lg16z2U"
    },
    "outputs": [],
    "source": [
-    "torch.save(classifier.state_dict(), './weight_model')"
+    "torch.save(classifier.state_dict(), 'pytorch_model_w.bin')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 51,
    "id": "j8BGaz9-Ni45",
    "metadata": {
     "id": "j8BGaz9-Ni45"
    },
    "outputs": [],
    "source": [
-    "classifier.save_pretrained('./models')"
+    "classifier.save_pretrained('my_model')"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "4cb346f1-a844-45cd-b8c5-6599d6130372",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "ad3de351-65e3-489f-9ed5-36520ba6bf40",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_42443/2240823436.py:2: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  state_dict = torch.load('YSDA_ML/pytorch_model.bin')\n"
+     ]
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "a8bee1ec-c5d3-4254-b9cb-139d29a104d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<All keys matched successfully>"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "id": "b20daefd",
    "metadata": {
-    "id": "b20daefd",
-    "jp-MarkdownHeadingCollapsed": true
+    "id": "b20daefd"
    },
    "source": [
     "## Что можно доделать?"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "47ba5a18-5c78-44f4-95d5-f55aa7a75fb2",
+   "metadata": {},
+   "source": [
+    "- Взять более сильную модель 1b deepseek (у меня не завелся на hf c ходу, хотя время на обучения потратил, правда через lora adapter)\n",
+    "- Взять большую обучающую выборку | нагенерировать синтетику через ChatGPT/DeepSeek\n",
+    "- Большую модель можно квантизовать + спарсификация, что в терии поможет ее прогонять на мобильных устройствах\n",
+    "- Нормальный deploy: triton or vllm "
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 55,
@@ -1220,16 +1118,6 @@
     "#         int_line.append(155)\n",
     "#     tags[i] = int_line"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1149f4e1",
-   "metadata": {
-    "id": "1149f4e1"
-   },
-   "source": [
-    "какой должен быть пайплайн на сервере: я беру данные склеиваю их + предобученныЙ токенайзер(я не буду загружать для него специальный файл т к я никак его не дообучаю) + кладу его в свою модель "
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