{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7018890b-b220-48a3-a1b8-d8f6c5483f6c",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'gpt-neo-125M'...\n",
      "remote: Enumerating objects: 65, done.\u001b[K\n",
      "remote: Counting objects: 100% (5/5), done.\u001b[K\n",
      "remote: Compressing objects: 100% (5/5), done.\u001b[K\n",
      "remote: Total 65 (delta 1), reused 0 (delta 0), pack-reused 60 (from 1)\u001b[K\n",
      "Unpacking objects: 100% (65/65), 1.11 MiB | 9.13 MiB/s, done.\n",
      "Filtering content: 100% (4/4), 1.93 GiB | 54.88 MiB/s, done.\n"
     ]
    }
   ],
   "source": [
    "!git clone https://huggingface.co/EleutherAI/gpt-neo-125M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "36078c20-4185-4f45-84f2-d3e95e1dcac9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mDEPRECATION: Loading egg at /usr/local/lib/python3.12/dist-packages/nvfuser-0.2.13a0+0d33366-py3.12-linux-x86_64.egg is deprecated. pip 25.1 will enforce this behaviour change. A possible replacement is to use pip for package installation. Discussion can be found at https://github.com/pypa/pip/issues/12330\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mDEPRECATION: Loading egg at /usr/local/lib/python3.12/dist-packages/dill-0.3.9-py3.12.egg is deprecated. pip 25.1 will enforce this behaviour change. A possible replacement is to use pip for package installation. Discussion can be found at https://github.com/pypa/pip/issues/12330\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mDEPRECATION: Loading egg at /usr/local/lib/python3.12/dist-packages/lightning_utilities-0.11.8-py3.12.egg is deprecated. pip 25.1 will enforce this behaviour change. A possible replacement is to use pip for package installation. Discussion can be found at https://github.com/pypa/pip/issues/12330\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mDEPRECATION: Loading egg at /usr/local/lib/python3.12/dist-packages/opt_einsum-3.4.0-py3.12.egg is deprecated. pip 25.1 will enforce this behaviour change. A possible replacement is to use pip for package installation. Discussion can be found at https://github.com/pypa/pip/issues/12330\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mDEPRECATION: Loading egg at /usr/local/lib/python3.12/dist-packages/igraph-0.11.8-py3.12-linux-x86_64.egg is deprecated. pip 25.1 will enforce this behaviour change. A possible replacement is to use pip for package installation. Discussion can be found at https://github.com/pypa/pip/issues/12330\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mDEPRECATION: Loading egg at /usr/local/lib/python3.12/dist-packages/lightning_thunder-0.2.0.dev0-py3.12.egg is deprecated. pip 25.1 will enforce this behaviour change. A possible replacement is to use pip for package installation. Discussion can be found at https://github.com/pypa/pip/issues/12330\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mDEPRECATION: Loading egg at /usr/local/lib/python3.12/dist-packages/texttable-1.7.0-py3.12.egg is deprecated. pip 25.1 will enforce this behaviour change. A possible replacement is to use pip for package installation. Discussion can be found at https://github.com/pypa/pip/issues/12330\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mDEPRECATION: Loading egg at /usr/local/lib/python3.12/dist-packages/looseversion-1.3.0-py3.12.egg is deprecated. pip 25.1 will enforce this behaviour change. A possible replacement is to use pip for package installation. Discussion can be found at https://github.com/pypa/pip/issues/12330\u001b[0m\u001b[33m\n",
      "\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install accelerate>=0.26.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "11d0a83e-391e-4590-9db6-6a2a3c140ae2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mDEPRECATION: Loading egg at /usr/local/lib/python3.12/dist-packages/nvfuser-0.2.13a0+0d33366-py3.12-linux-x86_64.egg is deprecated. pip 25.1 will enforce this behaviour change. A possible replacement is to use pip for package installation. Discussion can be found at https://github.com/pypa/pip/issues/12330\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mDEPRECATION: Loading egg at /usr/local/lib/python3.12/dist-packages/dill-0.3.9-py3.12.egg is deprecated. pip 25.1 will enforce this behaviour change. A possible replacement is to use pip for package installation. Discussion can be found at https://github.com/pypa/pip/issues/12330\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mDEPRECATION: Loading egg at /usr/local/lib/python3.12/dist-packages/lightning_utilities-0.11.8-py3.12.egg is deprecated. pip 25.1 will enforce this behaviour change. A possible replacement is to use pip for package installation. Discussion can be found at https://github.com/pypa/pip/issues/12330\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mDEPRECATION: Loading egg at /usr/local/lib/python3.12/dist-packages/opt_einsum-3.4.0-py3.12.egg is deprecated. pip 25.1 will enforce this behaviour change. A possible replacement is to use pip for package installation. Discussion can be found at https://github.com/pypa/pip/issues/12330\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mDEPRECATION: Loading egg at /usr/local/lib/python3.12/dist-packages/igraph-0.11.8-py3.12-linux-x86_64.egg is deprecated. pip 25.1 will enforce this behaviour change. A possible replacement is to use pip for package installation. Discussion can be found at https://github.com/pypa/pip/issues/12330\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mDEPRECATION: Loading egg at /usr/local/lib/python3.12/dist-packages/lightning_thunder-0.2.0.dev0-py3.12.egg is deprecated. pip 25.1 will enforce this behaviour change. A possible replacement is to use pip for package installation. Discussion can be found at https://github.com/pypa/pip/issues/12330\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mDEPRECATION: Loading egg at /usr/local/lib/python3.12/dist-packages/texttable-1.7.0-py3.12.egg is deprecated. pip 25.1 will enforce this behaviour change. A possible replacement is to use pip for package installation. Discussion can be found at https://github.com/pypa/pip/issues/12330\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mDEPRECATION: Loading egg at /usr/local/lib/python3.12/dist-packages/looseversion-1.3.0-py3.12.egg is deprecated. pip 25.1 will enforce this behaviour change. A possible replacement is to use pip for package installation. Discussion can be found at https://github.com/pypa/pip/issues/12330\u001b[0m\u001b[33m\n",
      "\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install datasets transformers torch gradio --quiet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7057ed8b-c98c-446e-b40b-2de8a800cb7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.12/dist-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
    "import torch\n",
    "\n",
    "# Ścieżka do lokalnego modelu\n",
    "local_model_path = \"./gpt-neo-125M\"\n",
    "\n",
    "# Załaduj tokenizer i model w trybie offline\n",
    "tokenizer = AutoTokenizer.from_pretrained(local_model_path, local_files_only=True)\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "model = AutoModelForCausalLM.from_pretrained(local_model_path, local_files_only=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3575956e-aa32-4173-8678-9a34b838ce3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Map: 100%|██████████| 20/20 [00:00<00:00, 1442.80 examples/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset({\n",
      "    features: ['input_ids', 'attention_mask'],\n",
      "    num_rows: 20\n",
      "})\n",
      "Liczba próbek po tokenizacji: 20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from datasets import Dataset\n",
    "from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer, DataCollatorForLanguageModeling\n",
    "\n",
    "# Ścieżka do lokalnego modelu\n",
    "local_model_path = \"./gpt-neo-125M\"\n",
    "\n",
    "# Tokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained(local_model_path, local_files_only=True)\n",
    "tokenizer.pad_token = tokenizer.eos_token  # <-- wymagane do paddingu\n",
    "\n",
    "# Wczytanie danych z pliku\n",
    "with open(\"data.txt\", encoding=\"utf-8\") as f:\n",
    "    lines = [line.strip() for line in f if line.strip()]  # Usuwamy puste linie\n",
    "\n",
    "# Budujemy Dataset z listy słowników\n",
    "data = [{\"text\": line} for line in lines]\n",
    "raw_dataset = Dataset.from_list(data)\n",
    "\n",
    "# Funkcja tokenizująca\n",
    "def tokenize_function(example):\n",
    "    return tokenizer(\n",
    "        example[\"text\"],\n",
    "        truncation=True,\n",
    "        max_length=128,\n",
    "        padding=\"max_length\"\n",
    "    )\n",
    "\n",
    "# Tokenizacja datasetu\n",
    "tokenized_dataset = raw_dataset.map(tokenize_function, batched=True, remove_columns=[\"text\"])\n",
    "\n",
    "# Sprawdź czy dane są OK\n",
    "print(tokenized_dataset)\n",
    "print(f\"Liczba próbek po tokenizacji: {len(tokenized_dataset)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c5eedf83-e003-434e-af7c-a57d6efdd120",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_6455/2971221750.py:23: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n",
      "  trainer = Trainer(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='60' max='60' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [60/60 00:12, Epoch 3/3]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>2.103000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "('./gpt-neo-finetuned/tokenizer_config.json',\n",
       " './gpt-neo-finetuned/special_tokens_map.json',\n",
       " './gpt-neo-finetuned/vocab.json',\n",
       " './gpt-neo-finetuned/merges.txt',\n",
       " './gpt-neo-finetuned/added_tokens.json',\n",
       " './gpt-neo-finetuned/tokenizer.json')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Wczytanie modelu\n",
    "model = AutoModelForCausalLM.from_pretrained(local_model_path, local_files_only=True)\n",
    "\n",
    "# Collator (bez maskowania)\n",
    "data_collator = DataCollatorForLanguageModeling(\n",
    "    tokenizer=tokenizer,\n",
    "    mlm=False\n",
    ")\n",
    "\n",
    "# Argumenty treningowe\n",
    "training_args = TrainingArguments(\n",
    "    output_dir=\"./gpt-neo-finetuned\",\n",
    "    overwrite_output_dir=True,\n",
    "    per_device_train_batch_size=1,\n",
    "    num_train_epochs=3,\n",
    "    save_steps=500,\n",
    "    logging_steps=50,\n",
    "    prediction_loss_only=True,\n",
    "    fp16=True\n",
    ")\n",
    "\n",
    "# Tworzymy Trainer\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=tokenized_dataset,  # <-- to była literówka, nie \"train_dataset\"\n",
    "    tokenizer=tokenizer,\n",
    "    data_collator=data_collator\n",
    ")\n",
    "\n",
    "# Start treningu\n",
    "trainer.train()\n",
    "\n",
    "# Zapis modelu i tokenizer\n",
    "trainer.save_model(\"./gpt-neo-finetuned\")\n",
    "tokenizer.save_pretrained(\"./gpt-neo-finetuned\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0d5c46d3-e115-4a68-9337-1f20b62a21a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_path = \"./gpt-neo-finetuned\"\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_path)\n",
    "model = AutoModelForCausalLM.from_pretrained(model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e656cc27-3748-4530-9ae5-05b2f8e9e105",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* Running on local URL:  http://127.0.0.1:7863\n",
      "* Running on public URL: https://596aa820b4401f3637.gradio.live\n",
      "\n",
      "This share link expires in 1 week. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"https://596aa820b4401f3637.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gradio as gr\n",
    "\n",
    "def chat(message, chat_history):\n",
    "    if chat_history:\n",
    "        prompt = chat_history + f\"\\nUser: {message}\\nAI:\"\n",
    "    else:\n",
    "        prompt = f\"User: {message}\\nAI:\"\n",
    "    \n",
    "    inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
    "    outputs = model.generate(\n",
    "        **inputs,\n",
    "        max_length=len(inputs[\"input_ids\"][0]) + 200,\n",
    "        temperature=0.7,\n",
    "        pad_token_id=tokenizer.eos_token_id,\n",
    "        do_sample=True,\n",
    "        top_p=0.9\n",
    "    )\n",
    "    full_response = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
    "    response = full_response.split(\"AI:\")[-1].strip()\n",
    "    chat_history += f\"\\nUser: {message}\\nAI: {response}\"\n",
    "    return chat_history, chat_history\n",
    "\n",
    "# Gradio UI w trybie notebookowym\n",
    "with gr.Blocks() as demo:\n",
    "    gr.Markdown(\"### Lokalny Czat z GPT\")\n",
    "    chatbot_output = gr.Textbox(label=\"Historia rozmowy\", lines=20, interactive=False)\n",
    "    user_input = gr.Textbox(label=\"Twoje pytanie\", placeholder=\"Zadaj pytanie i naciśnij Enter\")\n",
    "    state = gr.State(\"\")\n",
    "\n",
    "    user_input.submit(chat, [user_input, state], [chatbot_output, state])\n",
    "    user_input.submit(lambda: \"\", None, user_input)  # Czyści input po wysłaniu\n",
    "\n",
    "demo.launch(inline=True,share=True)"
   ]
  }
 ],
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   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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