fine-tuning-llama-3-with-simple-calculation.ipynb

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   "source": [
    "# Fine-tunning Llama 3 with simple calculation\n",
    "\n",
    "This is a fine tuning notes about using simple calculation dataset.\n",
    "\n",
    "\n",
    "# Environment prepare"
   ]
  },
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   "source": [
    "%%capture\n",
    "%pip install -U transformers \n",
    "%pip install -U datasets \n",
    "%pip install -U accelerate \n",
    "%pip install -U peft \n",
    "%pip install -U trl \n",
    "%pip install -U bitsandbytes \n",
    "%pip install -U wandb"
   ]
  },
  {
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   "source": [
    "from transformers import (\n",
    "    AutoModelForCausalLM,\n",
    "    AutoTokenizer,\n",
    "    BitsAndBytesConfig,\n",
    "    HfArgumentParser,\n",
    "    TrainingArguments,\n",
    "    pipeline,\n",
    "    logging,\n",
    ")\n",
    "from peft import (\n",
    "    LoraConfig,\n",
    "    PeftModel,\n",
    "    prepare_model_for_kbit_training,\n",
    "    get_peft_model,\n",
    ")\n",
    "import os, torch, wandb\n",
    "from datasets import load_dataset\n",
    "from trl import SFTTrainer, setup_chat_format"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23383129",
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   },
   "source": [
    "# Token and API\n",
    "\n",
    "We’ll be tracking the training process using the Weights & Biases and then saving the fine-tuned model on Hugging Face, and for that, we have to log in to both Hugging Face Hub and Weights & Biases using the API key."
   ]
  },
  {
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   "id": "63ce61d6",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n",
      "Token is valid (permission: read).\n",
      "Your token has been saved to /root/.cache/huggingface/token\n",
      "Login successful\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend.  Please refer to https://wandb.me/wandb-core for more information.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mzmicost\u001b[0m (\u001b[33mskywardai\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m If you're specifying your api key in code, ensure this code is not shared publicly.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Consider setting the WANDB_API_KEY environment variable, or running `wandb login` from the command line.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.19.0\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/kaggle/working/wandb/run-20241208_050306-xq93dype\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mquiet-hill-32\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/skywardai/Fine-tune%20Llama%203%208B%20on%20simple%20calculation\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/skywardai/Fine-tune%20Llama%203%208B%20on%20simple%20calculation/runs/xq93dype\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "from huggingface_hub import login\n",
    "from kaggle_secrets import UserSecretsClient\n",
    "user_secrets = UserSecretsClient()\n",
    "\n",
    "hf_token = user_secrets.get_secret(\"HUGGINGFACE_TOKEN\")\n",
    "\n",
    "login(token = hf_token)\n",
    "\n",
    "wb_token = user_secrets.get_secret(\"WANDB_API_KEY\")\n",
    "\n",
    "wandb.login(key=wb_token)\n",
    "run = wandb.init(\n",
    "    project='Fine-tune Llama 3 8B on simple calculation', \n",
    "    job_type=\"training\", \n",
    "    anonymous=\"allow\"\n",
    ")"
   ]
  },
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Base model\n",
    "\n",
    "Set the base model, dataset, and new model variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "207e722e",
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   "source": [
    "base_model = \"meta-llama/Meta-Llama-3-8B\"\n",
    "dataset_name = \"micost/simple_calculation\"\n",
    "new_model = \"llama-3-8b-chat-doctor\""
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Set the data type and attention implementation."
   ]
  },
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   "id": "2b4e1507",
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   "source": [
    "torch_dtype = torch.float16\n",
    "attn_implementation = \"eager\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad246158",
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   "source": [
    "# Loading the model and tokenizer\n",
    "\n",
    "In this part, we’ll load the model from Kaggle. However, due to memory constraints, we’re unable to load the full model. Therefore, we’re loading the model using 4-bit precision.\n",
    "\n",
    "Our goal in this project is to reduce memory usage and speed up the fine-tuning process."
   ]
  },
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      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
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     "metadata": {},
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    {
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      "text/plain": [
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     },
     "metadata": {},
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   ],
   "source": [
    "# QLoRA config\n",
    "bnb_config = BitsAndBytesConfig(\n",
    "    load_in_4bit=True,\n",
    "    bnb_4bit_quant_type=\"nf4\",\n",
    "    bnb_4bit_compute_dtype=torch_dtype,\n",
    "    bnb_4bit_use_double_quant=True,\n",
    ")\n",
    "\n",
    "# Load model\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    base_model,\n",
    "    quantization_config=bnb_config,\n",
    "    device_map=\"auto\",\n",
    "    attn_implementation=attn_implementation\n",
    ")"
   ]
  },
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   "outputs": [
    {
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       "tokenizer_config.json:   0%|          | 0.00/50.6k [00:00<?, ?B/s]"
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      "text/plain": [
       "special_tokens_map.json:   0%|          | 0.00/73.0 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The new embeddings will be initialized from a multivariate normal distribution that has old embeddings' mean and covariance. As described in this article: https://nlp.stanford.edu/~johnhew/vocab-expansion.html. To disable this, use `mean_resizing=False`\n",
      "The new lm_head weights will be initialized from a multivariate normal distribution that has old embeddings' mean and covariance. As described in this article: https://nlp.stanford.edu/~johnhew/vocab-expansion.html. To disable this, use `mean_resizing=False`\n"
     ]
    }
   ],
   "source": [
    "# Load tokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained(base_model)\n",
    "model, tokenizer = setup_chat_format(model, tokenizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d93892a",
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    },
    "tags": []
   },
   "source": [
    "# Adding the adapter to the layer\n",
    "\n",
    "Fine-tuning the full model will take a lot of time, so to improve the training time, we’ll attach the adapter layer with a few parameters, making the entire process faster and more memory-efficient."
   ]
  },
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   "source": [
    "# LoRA config\n",
    "peft_config = LoraConfig(\n",
    "    r=16,\n",
    "    lora_alpha=32,\n",
    "    lora_dropout=0.05,\n",
    "    bias=\"none\",\n",
    "    task_type=\"CAUSAL_LM\",\n",
    "    target_modules=['up_proj', 'down_proj', 'gate_proj', 'k_proj', 'q_proj', 'v_proj', 'o_proj']\n",
    ")\n",
    "model = get_peft_model(model, peft_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3cefd14",
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    "tags": []
   },
   "source": [
    "# Loading the dataset\n",
    "\n",
    "To load and pre-process our dataset, we:\n",
    "\n",
    "1. Load the ruslanmv/ai-medical-chatbot dataset, shuffle it, and select only the top 1000 rows. This will significantly reduce the training time.\n",
    "\n",
    "2. Format the chat template to make it conversational. Combine the patient questions and doctor responses into a \"text\" column.\n",
    "\n",
    "3. Display a sample from the text column (the “text” column has a chat-like format with special tokens)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "093eb5de",
   "metadata": {
    "execution": {
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "247f75e138c449449dcae57841a0c3ec",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "README.md:   0%|          | 0.00/351 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f0fe4d5ded6437986bbd8ed040d6a38",
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       "version_minor": 0
      },
      "text/plain": [
       "train-00000-of-00001.parquet:   0%|          | 0.00/1.34M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1d5cbc19f3204c36b3b3c2f9829df5ca",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split:   0%|          | 0/50000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/multiprocess/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
      "  self.pid = os.fork()\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fff87f3a95a7432e81245cc22fb68bbf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map (num_proc=4):   0%|          | 0/1000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'<|im_start|>user\\n{\"A\":7060,\"op\":\"*\",\"B\":2760}<|im_end|>\\n<|im_start|>assistant\\n{\"result\": \"19485600\"}<|im_end|>\\n'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Importing the dataset\n",
    "dataset = load_dataset(dataset_name, split=\"all\")\n",
    "dataset = dataset.shuffle(seed=65).select(range(1000)) # Only use 1000 samples for quick demo\n",
    "\n",
    "def format_chat_template(row):\n",
    "    row_json = [{\"role\": \"user\", \"content\": row[\"input\"]},\n",
    "               {\"role\": \"assistant\", \"content\": row[\"output\"]}]\n",
    "    row[\"text\"] = tokenizer.apply_chat_template(row_json, tokenize=False)\n",
    "    return row\n",
    "\n",
    "dataset = dataset.map(\n",
    "    format_chat_template,\n",
    "    num_proc=4,\n",
    ")\n",
    "\n",
    "dataset['text'][3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9824b2f",
   "metadata": {
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     "duration": 0.006061,
     "end_time": "2024-12-08T05:11:35.348007",
     "exception": false,
     "start_time": "2024-12-08T05:11:35.341946",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Split the dataset into a training and validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7d3f1205",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-08T05:11:35.361729Z",
     "iopub.status.busy": "2024-12-08T05:11:35.361083Z",
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     "shell.execute_reply": "2024-12-08T05:11:35.375618Z"
    },
    "papermill": {
     "duration": 0.023911,
     "end_time": "2024-12-08T05:11:35.377942",
     "exception": false,
     "start_time": "2024-12-08T05:11:35.354031",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dataset = dataset.train_test_split(test_size=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "989d2070",
   "metadata": {
    "papermill": {
     "duration": 0.006059,
     "end_time": "2024-12-08T05:11:35.390363",
     "exception": false,
     "start_time": "2024-12-08T05:11:35.384304",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Complaining and training the model\n",
    "\n",
    "We are setting the model hyperparameters so that we can run it on the Kaggle. You can learn about each hyperparameter by reading the Fine-Tuning Llama 2 tutorial.\n",
    "\n",
    "We are fine-tuning the model for one epoch and logging the metrics using the Weights and Biases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "68066911",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2024-12-08T05:11:35.403728Z",
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     "shell.execute_reply": "2024-12-08T05:11:35.442606Z"
    },
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     "duration": 0.048638,
     "end_time": "2024-12-08T05:11:35.445190",
     "exception": false,
     "start_time": "2024-12-08T05:11:35.396552",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/transformers/training_args.py:1568: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "training_arguments = TrainingArguments(\n",
    "    output_dir=new_model,\n",
    "    per_device_train_batch_size=1,\n",
    "    per_device_eval_batch_size=1,\n",
    "    gradient_accumulation_steps=2,\n",
    "    optim=\"paged_adamw_32bit\",\n",
    "    num_train_epochs=5,\n",
    "    evaluation_strategy=\"steps\",\n",
    "    eval_steps=0.2,\n",
    "    logging_steps=1,\n",
    "    warmup_steps=10,\n",
    "    logging_strategy=\"steps\",\n",
    "    learning_rate=2e-4,\n",
    "    fp16=False,\n",
    "    bf16=False,\n",
    "    group_by_length=True,\n",
    "    report_to=\"wandb\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffeee80f",
   "metadata": {
    "papermill": {
     "duration": 0.00613,
     "end_time": "2024-12-08T05:11:35.457906",
     "exception": false,
     "start_time": "2024-12-08T05:11:35.451776",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We’ll now set up a supervised fine-tuning (SFT) trainer and provide a train and evaluation dataset, LoRA configuration, training argument, tokenizer, and model. We’re keeping the max_seq_length to 512 to avoid exceeding GPU memory during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "213b5823",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-08T05:11:35.471519Z",
     "iopub.status.busy": "2024-12-08T05:11:35.471218Z",
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     "shell.execute_reply": "2024-12-08T05:11:36.604511Z"
    },
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     "duration": 1.143375,
     "end_time": "2024-12-08T05:11:36.607562",
     "exception": false,
     "start_time": "2024-12-08T05:11:35.464187",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/huggingface_hub/utils/_deprecation.py:100: FutureWarning: Deprecated argument(s) used in '__init__': max_seq_length, dataset_text_field. Will not be supported from version '0.13.0'.\n",
      "\n",
      "Deprecated positional argument(s) used in SFTTrainer, please use the SFTConfig to set these arguments instead.\n",
      "  warnings.warn(message, FutureWarning)\n",
      "/opt/conda/lib/python3.10/site-packages/transformers/training_args.py:1568: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/trl/trainer/sft_trainer.py:300: UserWarning: You passed a `max_seq_length` argument to the SFTTrainer, the value you passed will override the one in the `SFTConfig`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/trl/trainer/sft_trainer.py:328: UserWarning: You passed a `dataset_text_field` argument to the SFTTrainer, the value you passed will override the one in the `SFTConfig`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "77698bbe06a34e0f82ddf0a95a9e2a65",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/900 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f135ceb421e24055aab174eda407dad1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/100 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainer = SFTTrainer(\n",
    "    model=model,\n",
    "    train_dataset=dataset[\"train\"],\n",
    "    eval_dataset=dataset[\"test\"],\n",
    "    peft_config=peft_config,\n",
    "    max_seq_length=512,\n",
    "    dataset_text_field=\"text\",\n",
    "    tokenizer=tokenizer,\n",
    "    args=training_arguments,\n",
    "    packing= False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "809fea2d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-08T05:11:36.622749Z",
     "iopub.status.busy": "2024-12-08T05:11:36.622117Z",
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     "shell.execute_reply": "2024-12-08T06:02:12.890428Z"
    },
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     "duration": 3036.285865,
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     "exception": false,
     "start_time": "2024-12-08T05:11:36.614626",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='2250' max='2250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [2250/2250 50:33, Epoch 5/5]\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>450</td>\n",
       "      <td>1.588100</td>\n",
       "      <td>1.943062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>900</td>\n",
       "      <td>1.467400</td>\n",
       "      <td>1.952981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1350</td>\n",
       "      <td>1.596900</td>\n",
       "      <td>1.824365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1800</td>\n",
       "      <td>1.534600</td>\n",
       "      <td>1.921153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2250</td>\n",
       "      <td>1.412100</td>\n",
       "      <td>1.968504</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n",
      "Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n",
      "/opt/conda/lib/python3.10/site-packages/peft/utils/save_and_load.py:260: UserWarning: Setting `save_embedding_layers` to `True` as the embedding layer has been resized during finetuning.\n",
      "  warnings.warn(\n",
      "Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n",
      "Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n",
      "/opt/conda/lib/python3.10/site-packages/peft/utils/save_and_load.py:260: UserWarning: Setting `save_embedding_layers` to `True` as the embedding layer has been resized during finetuning.\n",
      "  warnings.warn(\n",
      "Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n",
      "Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n",
      "/opt/conda/lib/python3.10/site-packages/peft/utils/save_and_load.py:260: UserWarning: Setting `save_embedding_layers` to `True` as the embedding layer has been resized during finetuning.\n",
      "  warnings.warn(\n",
      "Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n",
      "Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n",
      "/opt/conda/lib/python3.10/site-packages/peft/utils/save_and_load.py:260: UserWarning: Setting `save_embedding_layers` to `True` as the embedding layer has been resized during finetuning.\n",
      "  warnings.warn(\n",
      "Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n",
      "Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n",
      "/opt/conda/lib/python3.10/site-packages/peft/utils/save_and_load.py:260: UserWarning: Setting `save_embedding_layers` to `True` as the embedding layer has been resized during finetuning.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=2250, training_loss=1.621979601542155, metrics={'train_runtime': 3035.7246, 'train_samples_per_second': 1.482, 'train_steps_per_second': 0.741, 'total_flos': 6889090200207360.0, 'train_loss': 1.621979601542155, 'epoch': 5.0})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91c58f85",
   "metadata": {
    "papermill": {
     "duration": 0.007255,
     "end_time": "2024-12-08T06:02:12.915224",
     "exception": false,
     "start_time": "2024-12-08T06:02:12.907969",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Model evaluation\n",
    "\n",
    "When you finish the Weights & Biases session, it’ll generate the run history and summary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4f9e4de9",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m:                                                                                \n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Run history:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:               eval/loss ▇▇▁▆█\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:            eval/runtime █▁▂▅▃\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: eval/samples_per_second ▁██▅▆\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:   eval/steps_per_second ▁██▅▆\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:             train/epoch ▁▁▁▁▁▂▂▂▂▂▃▃▃▃▄▄▄▄▄▄▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇▇▇▇██\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:       train/global_step ▁▂▂▂▂▂▂▂▃▃▃▃▄▄▄▄▄▄▄▄▅▅▅▅▅▅▅▅▆▆▆▇▇▇▇▇▇███\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:         train/grad_norm ▂▂▃▁█▂▁▂▃▅▂▂▁▂▃▄▂▂▂▂▂▃▂▂▁▁▁▁▂▂▂▂▂▁▁▁▁▂▂▂\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:     train/learning_rate ▂█████▇▇▇▇▆▆▆▆▆▆▆▆▆▅▅▅▅▄▄▄▃▃▃▃▃▂▂▂▂▂▂▂▂▁\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:              train/loss █▃▅▆▃█▂▅▂▃▇▃▇▄▄▄▄▃▂▂▃▇▄▃▂▄▂▂▂▂▄▂▃▃▂▂▁▁▁▂\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Run summary:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:                eval/loss 1.9685\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:             eval/runtime 28.9491\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:  eval/samples_per_second 3.454\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:    eval/steps_per_second 3.454\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:               total_flos 6889090200207360.0\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:              train/epoch 5\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:        train/global_step 2250\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:          train/grad_norm 1.48281\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:      train/learning_rate 0\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:               train/loss 1.4121\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:               train_loss 1.62198\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:            train_runtime 3035.7246\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: train_samples_per_second 1.482\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:   train_steps_per_second 0.741\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run \u001b[33mquiet-hill-32\u001b[0m at: \u001b[34m\u001b[4mhttps://wandb.ai/skywardai/Fine-tune%20Llama%203%208B%20on%20simple%20calculation/runs/xq93dype\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at: \u001b[34m\u001b[4mhttps://wandb.ai/skywardai/Fine-tune%20Llama%203%208B%20on%20simple%20calculation\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Find logs at: \u001b[35m\u001b[1m./wandb/run-20241208_050306-xq93dype/logs\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "wandb.finish()\n",
    "model.config.use_cache = True"
   ]
  },
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'result': '802'}\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "def extract_result(text):\n",
    "  pattern = r'\\{.*\"result\":\\s*\"(\\d+)\".*\\}'\n",
    "  match = re.search(pattern, text)\n",
    "  if match:\n",
    "    return {\"result\": match.group(1)}\n",
    "  else:\n",
    "    return None\n",
    "\n",
    "messages = [\n",
    "    {\n",
    "        \"role\": \"user\",\n",
    "        \"content\": \"{ \\\"A\\\": \\”970\\\", \\\"B\\\": \\\"34\\\", \\\"op\\\": \\\"-\\\" }\"\n",
    "    }\n",
    "]\n",
    "\n",
    "prompt = tokenizer.apply_chat_template(messages, tokenize=False, \n",
    "                                       add_generation_prompt=True)\n",
    "\n",
    "inputs = tokenizer(prompt, return_tensors='pt', padding=True, \n",
    "                   truncation=True).to(\"cuda\")\n",
    "\n",
    "outputs = model.generate(**inputs, max_length=150, \n",
    "                         num_return_sequences=1)\n",
    "\n",
    "text = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
    "\n",
    "\n",
    "print(extract_result(text.split(\"assistant\")[1]))"
   ]
  },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19770640\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "def calculator(a, b, op, tokenizer, model):\n",
    "  messages = [\n",
    "    {\n",
    "      \"role\": \"user\",\n",
    "      \"content\": f\"{{\\\"A\\\": \\\"{a}\\\", \\\"B\\\": \\\"{b}\\\", \\\"op\\\": \\\"{op}\\\"}}\"\n",
    "    }\n",
    "  ]\n",
    "\n",
    "  prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n",
    "  inputs = tokenizer(prompt, return_tensors='pt',padding=True, truncation=True).to(\"cuda\")\n",
    "  outputs = model.generate(**inputs, max_length=150, num_return_sequences=1)\n",
    "  text = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
    "  result_str = extract_result(text.split(\"assistant\")[1])\n",
    "  #result_dict = json.loads(result_str)\n",
    "  result_value = result_str[\"result\"]\n",
    "  return result_value\n",
    "\n",
    "result = calculator(970, 34, \"-\", tokenizer, model)\n",
    "print(result)  # Output: 936"
   ]
  },
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     "text": [
      "/opt/conda/lib/python3.10/site-packages/peft/utils/save_and_load.py:260: UserWarning: Setting `save_embedding_layers` to `True` as the embedding layer has been resized during finetuning.\n",
      "  warnings.warn(\n"
     ]
    }
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    "trainer.model.save_pretrained(new_model)\n"
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