NLP/LLM-ALPACA-SPANISH/tranining_chatgtp_alpaca_lora.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-aE9fhQIZCOB"
   },
   "source": [
    "# Training a Model similar to ChatGPT using Alpaca-loca"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The notebook from my blogpost: https://armandolivares.tech/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install the required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tSMKeAMb4bnm",
    "outputId": "972d5030-d3d8-4613-b062-8a8316b6ec3c"
   },
   "outputs": [],
   "source": [
    "\n",
    "!pip -qq install datasets\n",
    "!pip -qq install bitsandbytes==0.37.2\n",
    "!pip -qq install six==1.16.0\n",
    "!pip -qq install -q datasets loralib sentencepiece\n",
    "!pip -qq install -q git+https://github.com/zphang/transformers@c3dc391\n",
    "!pip -qq install -q git+https://github.com/huggingface/peft.git\n",
    "!pip -qq install huggingface_hub\n",
    "!pip -qq install ipywidgets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BiNQHqyxZGL6",
    "outputId": "579af781-40c7-411f-ce5b-c4f630a64c91"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===================================BUG REPORT===================================\n",
      "Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
      "================================================================================\n",
      "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching /usr/local/cuda/lib64...\n",
      "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so\n",
      "CUDA SETUP: Highest compute capability among GPUs detected: 8.6\n",
      "CUDA SETUP: Detected CUDA version 118\n",
      "CUDA SETUP: Loading binary /home/usergpu/.local/lib/python3.8/site-packages/bitsandbytes/libbitsandbytes_cuda118.so...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/usergpu/.local/lib/python3.8/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/usr/local/cuda-10.0/bin')}\n",
      "  warn(msg)\n",
      "/home/usergpu/.local/lib/python3.8/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: /usr/local/cuda-11.8/bin:/usr/local/cuda-10.0/bin: did not contain libcudart.so as expected! Searching further paths...\n",
      "  warn(msg)\n",
      "/home/usergpu/.local/lib/python3.8/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('//matplotlib_inline.backend_inline'), PosixPath('module')}\n",
      "  warn(msg)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import bitsandbytes as bnb\n",
    "from datasets import load_dataset\n",
    "import transformers\n",
    "from transformers import AutoTokenizer, AutoConfig, LLaMAForCausalLM, LLaMATokenizer\n",
    "from peft import prepare_model_for_int8_training, LoraConfig, get_peft_model, get_peft_model_state_dict\n",
    "     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the translated data from the json file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 86,
     "referenced_widgets": [
      "9a6fff38ec104dfbb37f58e5b2e6ed73",
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      "920121c52db941e8b02f2ac0167a7164",
      "a2e3fc07813f4251b712afa84b5e505e"
     ]
    },
    "id": "dfHNgtsb4YPh",
    "outputId": "3ad91fc6-4fd9-4218-b3e5-19f6c6c24520"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset json (/home/usergpu/.cache/huggingface/datasets/json/default-3306e0878942785e/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "57906bf1a9914ead84c365d2af363dec",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datasets import load_dataset, Dataset\n",
    "\n",
    "data = load_dataset(\"json\", data_files=\"alpaca_data_translated.json\")\n",
    "\n",
    "data = data.shuffle()\n",
    "     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "r1bXKHPyV1to",
    "outputId": "d9056be8-e43e-4182-ed59-17a45365005c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['instruction', 'output', 'input'],\n",
       "        num_rows: 52002\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And check a sample:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'instruction': '¿Cuál es el precio promedio de un galón de gasolina regular sin plomo en los Estados Unidos?',\n",
       " 'output': 'A diciembre de 2020, el precio promedio de un galón de gasolina regular sin plomo en los Estados Unidos es de $2,51.',\n",
       " 'input': ''}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"train\"][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's set some model's parameters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "EcAp2ANwXyqd"
   },
   "outputs": [],
   "source": [
    "MICRO_BATCH_SIZE = 12  # use 4-5 \n",
    "BATCH_SIZE = 128\n",
    "GRADIENT_ACCUMULATION_STEPS = BATCH_SIZE // MICRO_BATCH_SIZE\n",
    "EPOCHS = 3  # \n",
    "LEARNING_RATE = 3e-4  # the Karpathy constant\n",
    "CUTOFF_LEN = 256  # 256 accounts for about 96% of the data\n",
    "LORA_R = 8\n",
    "LORA_ALPHA = 16\n",
    "LORA_DROPOUT = 0.05\n",
    "VAL_SET_SIZE=2000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split data set into train and validation data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "2S46yN5LV2qI"
   },
   "outputs": [],
   "source": [
    "\n",
    "train_val = data[\"train\"].train_test_split(\n",
    "    test_size=VAL_SET_SIZE, shuffle=True, seed=42\n",
    ")\n",
    "train_data = train_val[\"train\"]\n",
    "val_data = train_val[\"test\"]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zbk5OKLGX58t"
   },
   "source": [
    "# The model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 86,
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      "68f2af0dec354e869fc12e869350be9b"
     ]
    },
    "id": "qbqeJWYbXr2z",
    "outputId": "875b1b26-ac71-460e-e20f-98d3c115cb60"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Overriding torch_dtype=None with `torch_dtype=torch.float16` due to requirements of `bitsandbytes` to enable model loading in mixed int8. Either pass torch_dtype=torch.float16 or don't pass this argument at all to remove this warning.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e5f0659af3ad498d886979054ac69103",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/33 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = LLaMAForCausalLM.from_pretrained(\n",
    "    \"decapoda-research/llama-7b-hf\",\n",
    "    load_in_8bit=True,\n",
    "    device_map=\"auto\",\n",
    ")\n",
    "tokenizer = LLaMATokenizer.from_pretrained(\n",
    "    \"decapoda-research/llama-7b-hf\", add_eos_token=True\n",
    ")\n",
    "\n",
    "model = prepare_model_for_int8_training(model)\n",
    "\n",
    "config = LoraConfig(\n",
    "    r=LORA_R,\n",
    "    lora_alpha=LORA_ALPHA,\n",
    "    target_modules=[\"q_proj\", \"v_proj\"],\n",
    "    lora_dropout=LORA_DROPOUT,\n",
    "    bias=\"none\",\n",
    "    task_type=\"CAUSAL_LM\",\n",
    ")\n",
    "model = get_peft_model(model, config)\n",
    "tokenizer.pad_token_id = 0  # unk. we want this to be different from the eos token\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17,
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    },
    "id": "i78jy4GhX8MO",
    "outputId": "61bbe917-a7f0-442c-bcd7-0f8d3b675a94"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1c374ca86d8b4d77825df70abdeb22b3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/50002 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b5600b39a4f74c37855d167b655bb293",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/2000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "\n",
    "def generate_prompt(data_point):\n",
    "\n",
    "    if data_point[\"input\"]:\n",
    "        return f\"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
    "                ### Instruction:\n",
    "                {data_point[\"instruction\"]}\n",
    "                ### Input:\n",
    "                {data_point[\"input\"]}\n",
    "                ### Response:\n",
    "                {data_point[\"output\"]}\"\"\"\n",
    "    else:\n",
    "        return f\"\"\"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
    "                ### Instruction:\n",
    "                {data_point[\"instruction\"]}\n",
    "                ### Response:\n",
    "                {data_point[\"output\"]}\"\"\"\n",
    "\n",
    "\n",
    "def tokenize(prompt):\n",
    "    result = tokenizer(\n",
    "        prompt,\n",
    "        truncation=True,\n",
    "        max_length=CUTOFF_LEN + 1,\n",
    "        padding=\"max_length\",\n",
    "    )\n",
    "    return {\n",
    "        \"input_ids\": result[\"input_ids\"][:-1],\n",
    "        \"attention_mask\": result[\"attention_mask\"][:-1],\n",
    "    }\n",
    "\n",
    "\n",
    "train_data = train_data.shuffle().map(lambda x: tokenize(generate_prompt(x)))\n",
    "val_data = val_data.shuffle().map(lambda x: tokenize(generate_prompt(x)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "E0VuEo9WaZHv"
   },
   "outputs": [],
   "source": [
    "world_size = int(os.environ.get('WORLD_SIZE', 1))\n",
    "ddp = world_size != 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "2K9QtKZBazX1"
   },
   "outputs": [],
   "source": [
    "trainer = transformers.Trainer(\n",
    "    model=model,\n",
    "    train_dataset=train_data,\n",
    "    eval_dataset=val_data,\n",
    "    args=transformers.TrainingArguments(\n",
    "        per_device_train_batch_size=MICRO_BATCH_SIZE,\n",
    "        gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,\n",
    "        warmup_steps=100,\n",
    "        num_train_epochs=EPOCHS,\n",
    "        learning_rate=LEARNING_RATE,\n",
    "        fp16=True,\n",
    "        logging_steps=20,\n",
    "        evaluation_strategy=\"steps\",\n",
    "        save_strategy=\"steps\",\n",
    "        eval_steps=200,\n",
    "        save_steps=200,\n",
    "        output_dir=\"spa-lora-alpaca\",\n",
    "        save_total_limit=3,\n",
    "        load_best_model_at_end=True,\n",
    "        ddp_find_unused_parameters=False if ddp else None,\n",
    "    ),\n",
    "    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Start Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 188
    },
    "id": "g7LaBgbsbUyA",
    "outputId": "6fabbbdd-d80b-44b8-f6ab-11bbbcd02bdb",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/usergpu/.local/lib/python3.8/site-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='1248' max='1248' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [1248/1248 5:53:46, Epoch 2/3]\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",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>200</td>\n",
       "      <td>0.969400</td>\n",
       "      <td>0.966256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>400</td>\n",
       "      <td>0.938500</td>\n",
       "      <td>0.942444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>600</td>\n",
       "      <td>0.933900</td>\n",
       "      <td>0.931122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>800</td>\n",
       "      <td>0.922800</td>\n",
       "      <td>0.923243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>0.895800</td>\n",
       "      <td>0.919768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1200</td>\n",
       "      <td>0.907300</td>\n",
       "      <td>0.916304</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=1248, training_loss=0.9616337785354028, metrics={'train_runtime': 21243.7431, 'train_samples_per_second': 7.061, 'train_steps_per_second': 0.059, 'total_flos': 1.522526532277371e+18, 'train_loss': 0.9616337785354028, 'epoch': 3.0})"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.config.use_cache = False\n",
    "trainer.train()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the model to disk:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "55qKG2YjAYkm"
   },
   "outputs": [],
   "source": [
    "model.save_pretrained(\"spa-lora-alpaca\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VuNAesCF_agJ"
   },
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "KnDe9uY8-sTx"
   },
   "outputs": [],
   "source": [
    "\n",
    "from peft import PeftModel\n",
    "from transformers import LLaMATokenizer, LLaMAForCausalLM, GenerationConfig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "4vClUZJZ_dkp"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Overriding torch_dtype=None with `torch_dtype=torch.float16` due to requirements of `bitsandbytes` to enable model loading in mixed int8. Either pass torch_dtype=torch.float16 or don't pass this argument at all to remove this warning.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e6c58a4f01944a48b4b7919777344070",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/33 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tokenizer = LLaMATokenizer.from_pretrained(\"decapoda-research/llama-7b-hf\")\n",
    "model_custom = LLaMAForCausalLM.from_pretrained(\n",
    "    \"decapoda-research/llama-7b-hf\",\n",
    "    load_in_8bit=True,\n",
    "    device_map=\"auto\",\n",
    ")\n",
    "\n",
    "model_custom = PeftModel.from_pretrained(model_custom, \"spa-lora-alpaca\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "0jjvImx5baFA"
   },
   "outputs": [],
   "source": [
    "                  \n",
    "\n",
    "\n",
    "def generate_instruction_prompt(instruction, input=None):\n",
    "\n",
    "    if input:\n",
    "        return f\"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
    "                ### Instruction:\n",
    "                {instruction}\n",
    "                ### Input:\n",
    "                {input}\n",
    "                ### Response:\"\"\"\n",
    "    else:\n",
    "        return f\"\"\"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
    "                ### Instruction:\n",
    "                {instruction}\n",
    "                ### Response:\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "GG3k7pVS-bEQ"
   },
   "outputs": [],
   "source": [
    "generation_config = GenerationConfig(\n",
    "    temperature=0.1,\n",
    "    top_p=0.75,\n",
    "    num_beams=4,\n",
    ")\n",
    "\n",
    "def evaluate(model_aaa, instruction, input=None):\n",
    "    prompt = generate_instruction_prompt(instruction, input)\n",
    "    inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
    "    input_ids = inputs[\"input_ids\"].cuda()\n",
    "    generation_output = model_aaa.generate(\n",
    "        input_ids=input_ids,\n",
    "        generation_config=generation_config,\n",
    "        return_dict_in_generate=True,\n",
    "        output_scores=True,\n",
    "        max_new_tokens=256\n",
    "    )\n",
    "    for s in generation_output.sequences:\n",
    "        output = tokenizer.decode(s)\n",
    "        print(\"Response:\", output.split(\"### Response:\")[1].strip())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time for text generation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write me a little story:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "-qZBxxIJ-fkh"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: Jones vivía en las montañas. Era un hombre fuerte y valiente que siempre estaba dispuesto a ayudar a los demás. Un día, se encontró con un grupo de bandidos que estaban intentando robar un tesoro. Jones se enfrentó a los bandidos y los derrotó, salvando el tesoro y protegiendo a la gente de las montañas.\n"
     ]
    }
   ],
   "source": [
    "evaluate(model_custom,\"Crea una historia sobre un heroe llamado Jones que vivia en las montañas\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KqYV_B9vVPAn"
   },
   "source": [
    "### Give me some info:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: La diabetes es un trastorno metabólico causado por una deficiencia de insulina o resistencia a la insulina. La insulina es un hormona producida por el páncreas que ayuda a regular los niveles de azúcar en la sangre. La diabetes se caracteriza por altos niveles de azúcar en la sangre debido a la falta de insulina o la resistencia a la insulina.\n"
     ]
    }
   ],
   "source": [
    "evaluate(model_custom,\"Que es la diabetes?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Give me some advices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: 1. Asegúrese de mantenerse activo físicamente.\n",
      "2. Asegúrese de tomar descansos regulares.\n",
      "3. Asegúrese de mantenerse en contacto con amigos y familiares.\n"
     ]
    }
   ],
   "source": [
    "evaluate(model_custom,\"Enumera 3 consejos de vida\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What about writing code?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: def get_string_length(str):\n",
      "    volver len(str)\n"
     ]
    }
   ],
   "source": [
    "evaluate(model_custom,\"Escribe una function en python para revisar la longitud de un string\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: Napoleón Bonaparte fue un general y emperador francés.\n"
     ]
    }
   ],
   "source": [
    "evaluate(model_custom,\"Quien fue napoleon?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: Negativo\n"
     ]
    }
   ],
   "source": [
    "evaluate(model_custom, \"clafifica este tweet en positivo, negativo o neutro: Se ha convertido en el jugador más joven en debutar en LaLiga como azulgrana\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### These aren't bad at all!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display Model Info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The LLM model has 419 different named parameters.\n",
      "\n",
      "==== Embedding Layer ====\n",
      "\n",
      "base_model.model.model.embed_tokens.weight              (32000, 4096)\n",
      "base_model.model.model.layers.0.self_attn.q_proj.weight (4096, 4096)\n",
      "\n",
      "==== First Transformer ====\n",
      "\n",
      "base_model.model.model.layers.0.self_attn.q_proj.lora_A.default.weight    (8, 4096)\n",
      "base_model.model.model.layers.0.self_attn.q_proj.lora_B.default.weight    (4096, 8)\n",
      "base_model.model.model.layers.0.self_attn.k_proj.weight (4096, 4096)\n",
      "base_model.model.model.layers.0.self_attn.v_proj.weight (4096, 4096)\n",
      "base_model.model.model.layers.0.self_attn.v_proj.lora_A.default.weight    (8, 4096)\n",
      "base_model.model.model.layers.0.self_attn.v_proj.lora_B.default.weight    (4096, 8)\n",
      "base_model.model.model.layers.0.self_attn.o_proj.weight (4096, 4096)\n",
      "base_model.model.model.layers.0.mlp.gate_proj.weight    (11008, 4096)\n",
      "base_model.model.model.layers.0.mlp.down_proj.weight    (4096, 11008)\n",
      "base_model.model.model.layers.0.mlp.up_proj.weight      (11008, 4096)\n",
      "base_model.model.model.layers.0.input_layernorm.weight       (4096,)\n",
      "base_model.model.model.layers.0.post_attention_layernorm.weight      (4096,)\n",
      "\n",
      "==== Output Layer ====\n",
      "\n",
      "base_model.model.model.norm.weight                           (4096,)\n",
      "base_model.model.lm_head.weight                         (32000, 4096)\n"
     ]
    }
   ],
   "source": [
    "# Get all of the model's parameters as a list of tuples.\n",
    "params = list(model_custom.named_parameters())\n",
    "\n",
    "print('The LLM model has {:} different named parameters.\\n'.format(len(params)))\n",
    "\n",
    "print('==== Embedding Layer ====\\n')\n",
    "\n",
    "for p in params[0:2]:\n",
    "    print(\"{:<55} {:>12}\".format(p[0], str(tuple(p[1].size()))))\n",
    "\n",
    "print('\\n==== First Transformer ====\\n')\n",
    "\n",
    "for p in params[2:14]:\n",
    "    print(\"{:<55} {:>12}\".format(p[0], str(tuple(p[1].size()))))\n",
    "\n",
    "print('\\n==== Output Layer ====\\n')\n",
    "\n",
    "for p in params[-2:]:\n",
    "    print(\"{:<55} {:>12}\".format(p[0], str(tuple(p[1].size()))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Upload to Hugginface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Token will not been saved to git credential helper. Pass `add_to_git_credential=True` if you want to set the git credential as well.\n",
      "Token is valid.\n",
      "Your token has been saved to /home/usergpu/.cache/huggingface/token\n",
      "Login successful\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from huggingface_hub import login\n",
    "\n",
    "login(token=\"your_token\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Upload 1 LFS files:   0%|          | 0/1 [00:00<?, ?it/s]\n",
      "adapter_model.bin:   0%|          | 0.00/16.8M [00:00<?, ?B/s]\u001b[A\n",
      "adapter_model.bin:   0%|          | 8.19k/16.8M [00:00<09:18, 30.1kB/s]\u001b[A\n",
      "adapter_model.bin:   1%|          | 180k/16.8M [00:00<00:33, 500kB/s]  \u001b[A\n",
      "adapter_model.bin:   4%|▍         | 745k/16.8M [00:00<00:09, 1.68MB/s]\u001b[A\n",
      "adapter_model.bin:  11%|█▏        | 1.90M/16.8M [00:00<00:03, 3.99MB/s]\u001b[A\n",
      "adapter_model.bin:  28%|██▊       | 4.66M/16.8M [00:00<00:01, 7.98MB/s]\u001b[A\n",
      "adapter_model.bin:  57%|█████▋    | 9.63M/16.8M [00:01<00:00, 17.4MB/s]\u001b[A\n",
      "adapter_model.bin:  74%|███████▍  | 12.4M/16.8M [00:01<00:00, 19.1MB/s]\u001b[A\n",
      "adapter_model.bin: 100%|██████████| 16.8M/16.8M [00:01<00:00, 10.6MB/s]\u001b[A\n",
      "Upload 1 LFS files: 100%|██████████| 1/1 [00:01<00:00,  1.90s/it]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "CommitInfo(commit_url='https://huggingface.co/Armandoliv/spa-alpaca-lora/commit/ff954c99c1d32431ff64f78c40a7a96ee760c204', commit_message='Upload model', commit_description='', oid='ff954c99c1d32431ff64f78c40a7a96ee760c204', pr_url=None, pr_revision=None, pr_num=None)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_custom.push_to_hub(\"Armandoliv/spa-alpaca-lora\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now, you can use the model directly from huggingface:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Overriding torch_dtype=None with `torch_dtype=torch.float16` due to requirements of `bitsandbytes` to enable model loading in mixed int8. Either pass torch_dtype=torch.float16 or don't pass this argument at all to remove this warning.\n",
      "Loading checkpoint shards: 100%|██████████| 33/33 [00:15<00:00,  2.16it/s]\n",
      "Downloading (…)/adapter_config.json: 100%|██████████| 350/350 [00:00<00:00, 33.9kB/s]\n",
      "Downloading adapter_model.bin: 100%|██████████| 16.8M/16.8M [00:01<00:00, 16.6MB/s]\n"
     ]
    }
   ],
   "source": [
    "tokenizer = LLaMATokenizer.from_pretrained(\"decapoda-research/llama-7b-hf\")\n",
    "model_custom = LLaMAForCausalLM.from_pretrained(\n",
    "    \"decapoda-research/llama-7b-hf\",\n",
    "    load_in_8bit=True,\n",
    "    device_map=\"auto\",\n",
    ")\n",
    "\n",
    "model_custom = PeftModel.from_pretrained(model_custom, \"Armandoliv/spa-alpaca-lora\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: Jones vivía en las montañas azules, una hermosa región de montañas y bosques. Era un valiente guerrero que siempre estaba dispuesto a ayudar a quienes lo necesitaban. Un día, Jones se encontró con una criatura misteriosa que le pidió que le ayudara a salir de las montañas azules. Jones aceptó la misión y se embarcó en una aventura para ayudar a la criatura a salir de las montañas azules. Durante su viaje, Jones se encontró con muchos peligros y desafíos, pero finalmente logró ayudar a la criatura a salir de las montañas azules.\n"
     ]
    }
   ],
   "source": [
    "evaluate(model_custom,\"Crea una historia sobre un heroe llamado Jones que vivia en las montañas azules\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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