llama-3-2.ipynb

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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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     "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: fineGrained).\n",
      "Your token has been saved to /root/.cache/huggingface/token\n",
      "Login successful\n"
     ]
    }
   ],
   "source": [
    "from huggingface_hub import login\n",
    "login(\"YOUR_HF_TOKEN\")"
   ]
  },
  {
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   "id": "0d7abbd2",
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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"
   ]
  },
  {
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     "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: fineGrained).\n",
      "Your token has been saved to /root/.cache/huggingface/token\n",
      "Login successful\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",
    "login(token = hf_token)"
   ]
  },
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     "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: W&B API key is configured. 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: Currently logged in as: \u001b[33mdyuva2005\u001b[0m (\u001b[33mdyuva2005-vellore-institute-of-technology\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.18.5\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/kaggle/working/wandb/run-20241026_072103-3n34i37m\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[33mstellar-bush-7\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/dyuva2005-vellore-institute-of-technology/Fine-tune%20Llama%203.2%20on%20Customer%20Support%20Dataset\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/dyuva2005-vellore-institute-of-technology/Fine-tune%20Llama%203.2%20on%20Customer%20Support%20Dataset/runs/3n34i37m\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "wb_token = user_secrets.get_secret(\"wandb\")\n",
    "\n",
    "wandb.login(key=wb_token)\n",
    "run = wandb.init(\n",
    "    project='Fine-tune Llama 3.2 on Customer Support Dataset', \n",
    "    job_type=\"training\", \n",
    "    anonymous=\"allow\"\n",
    ")"
   ]
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   "source": [
    "base_model = \"meta-llama/Llama-3.2-3B-Instruct\"\n",
    "new_model = \"llama-3.2-3b-it-Ecommerce-ChatBot\"\n",
    "dataset_name = \"bitext/Bitext-customer-support-llm-chatbot-training-dataset\""
   ]
  },
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   "source": [
    "if torch.cuda.get_device_capability()[0] >= 8:\n",
    "    !pip install -qqq flash-attn\n",
    "    torch_dtype = torch.bfloat16\n",
    "    attn_implementation = \"flash_attention_2\"\n",
    "else:\n",
    "    torch_dtype = torch.float16\n",
    "    attn_implementation = \"eager\""
   ]
  },
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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",
    "# 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",
    ")\n",
    "\n",
    "# Load tokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)"
   ]
  },
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       "(…)t_Training_Dataset_27K_responses-v11.csv:   0%|          | 0.00/19.2M [00:00<?, ?B/s]"
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       "Generating train split:   0%|          | 0/26872 [00:00<?, ? examples/s]"
      ]
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    {
     "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"
     ]
    },
    {
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      },
      "text/plain": [
       "Map (num_proc=4):   0%|          | 0/1000 [00:00<?, ? examples/s]"
      ]
     },
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    }
   ],
   "source": [
    "#Importing the dataset\n",
    "dataset = load_dataset(dataset_name, split=\"train\")\n",
    "dataset = dataset.shuffle(seed=65).select(range(1000)) # Only use 1000 samples for quick demo\n",
    "instruction = \"\"\"You are a top-rated customer service agent named John. \n",
    "    Be polite to customers and answer all their questions.\n",
    "    \"\"\"\n",
    "def format_chat_template(row):\n",
    "    \n",
    "    row_json = [{\"role\": \"system\", \"content\": instruction },\n",
    "               {\"role\": \"user\", \"content\": row[\"instruction\"]},\n",
    "               {\"role\": \"assistant\", \"content\": row[\"response\"]}]\n",
    "    \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"
   ]
  },
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     "data": {
      "text/plain": [
       "\"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\\n\\nCutting Knowledge Date: December 2023\\nToday Date: 26 Oct 2024\\n\\nYou are a top-rated customer service agent named John. \\n    Be polite to customers and answer all their questions.<|eot_id|><|start_header_id|>user<|end_header_id|>\\n\\ncould you tell me about the options for shipping?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\\n\\nOf course, I'd be delighted to provide you with information about our shipping options! Here are the various choices we offer:\\n\\n1. Standard Shipping: This option typically arrives within {{Date Range}} business days, catering to non-urgent items and ensuring a cost-effective delivery.\\n\\n2. Expedited Shipping: If you're looking for a faster option, choose expedited shipping. Your items will reach you within {{Date Range}} business days, offering a balance between speed and affordability.\\n\\n3. Overnight Shipping: For urgent needs, we have overnight shipping. This ensures your items are delivered on the next business day, offering the highest level of speed and convenience.\\n\\n4. In-Store Pickup: If you prefer a more hands-on approach, you can opt for in-store pickup. This option allows you to collect your items personally from one of our {{Store Location}}, offering added flexibility and convenience.\\n\\nRemember that specific delivery times may vary based on your location and other factors. If you have any further questions or need assistance with any aspect of our shipping options, please feel free to ask. We're here to ensure your satisfaction!<|eot_id|>\""
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['text'][3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fa996f2e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-26T07:23:52.824336Z",
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     "iopub.status.idle": "2024-10-26T07:23:52.831824Z",
     "shell.execute_reply": "2024-10-26T07:23:52.831128Z"
    },
    "papermill": {
     "duration": 0.019825,
     "end_time": "2024-10-26T07:23:52.833828",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import bitsandbytes as bnb\n",
    "\n",
    "def find_all_linear_names(model):\n",
    "    cls = bnb.nn.Linear4bit\n",
    "    lora_module_names = set()\n",
    "    for name, module in model.named_modules():\n",
    "        if isinstance(module, cls):\n",
    "            names = name.split('.')\n",
    "            lora_module_names.add(names[0] if len(names) == 1 else names[-1])\n",
    "    if 'lm_head' in lora_module_names:  # needed for 16 bit\n",
    "        lora_module_names.remove('lm_head')\n",
    "    return list(lora_module_names)\n",
    "\n",
    "modules = find_all_linear_names(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b7b0d3c5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-26T07:23:52.853195Z",
     "iopub.status.busy": "2024-10-26T07:23:52.852886Z",
     "iopub.status.idle": "2024-10-26T07:24:00.516442Z",
     "shell.execute_reply": "2024-10-26T07:24:00.515525Z"
    },
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     "duration": 7.676068,
     "end_time": "2024-10-26T07:24:00.518936",
     "exception": false,
     "start_time": "2024-10-26T07:23:52.842868",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "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"
     ]
    }
   ],
   "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=modules\n",
    ")\n",
    "model, tokenizer = setup_chat_format(model, tokenizer)\n",
    "model = get_peft_model(model, peft_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9e196811",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-26T07:24:00.542590Z",
     "iopub.status.busy": "2024-10-26T07:24:00.542237Z",
     "iopub.status.idle": "2024-10-26T07:24:00.576857Z",
     "shell.execute_reply": "2024-10-26T07:24:00.576007Z"
    },
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     "duration": 0.048455,
     "end_time": "2024-10-26T07:24:00.578950",
     "exception": false,
     "start_time": "2024-10-26T07:24:00.530495",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Hyperparamter\n",
    "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=1,\n",
    "    eval_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": "code",
   "execution_count": 14,
   "id": "7cc9429b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-26T07:24:00.599228Z",
     "iopub.status.busy": "2024-10-26T07:24:00.598483Z",
     "iopub.status.idle": "2024-10-26T07:24:00.613678Z",
     "shell.execute_reply": "2024-10-26T07:24:00.612784Z"
    },
    "papermill": {
     "duration": 0.027572,
     "end_time": "2024-10-26T07:24:00.615823",
     "exception": false,
     "start_time": "2024-10-26T07:24:00.588251",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_test_split = dataset.train_test_split(test_size=0.2)  # Adjust test_size as needed\n",
    "train_dataset = train_test_split[\"train\"]\n",
    "test_dataset = train_test_split[\"test\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "16f17305",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-26T07:24:00.637544Z",
     "iopub.status.busy": "2024-10-26T07:24:00.636952Z",
     "iopub.status.idle": "2024-10-26T07:24:02.433916Z",
     "shell.execute_reply": "2024-10-26T07:24:02.432964Z"
    },
    "papermill": {
     "duration": 1.809426,
     "end_time": "2024-10-26T07:24:02.436223",
     "exception": false,
     "start_time": "2024-10-26T07:24:00.626797",
     "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 '1.0.0'.\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/trl/trainer/sft_trainer.py:283: 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:321: 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": "a5f4e7c33ee847549c237ce1cdb6da10",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/800 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "56c78786e59d458a901fc67bb6c01bb5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/200 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/trl/trainer/sft_trainer.py:401: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `SFTTrainer.__init__`. Use `processing_class` instead.\n",
      "  super().__init__(\n"
     ]
    }
   ],
   "source": [
    "# Setting sft parameters\n",
    "trainer = SFTTrainer(\n",
    "    model=model,\n",
    "    train_dataset=train_dataset,\n",
    "    eval_dataset=test_dataset,\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": 16,
   "id": "976b4b69",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-26T07:24:02.457557Z",
     "iopub.status.busy": "2024-10-26T07:24:02.457010Z",
     "iopub.status.idle": "2024-10-26T07:32:54.225299Z",
     "shell.execute_reply": "2024-10-26T07:32:54.224310Z"
    },
    "papermill": {
     "duration": 531.780884,
     "end_time": "2024-10-26T07:32:54.227243",
     "exception": false,
     "start_time": "2024-10-26T07:24:02.446359",
     "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='400' max='400' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [400/400 08:48, Epoch 1/1]\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>80</td>\n",
       "      <td>0.762300</td>\n",
       "      <td>0.756096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>160</td>\n",
       "      <td>0.601000</td>\n",
       "      <td>0.691583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>240</td>\n",
       "      <td>0.542500</td>\n",
       "      <td>0.639155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>320</td>\n",
       "      <td>0.758000</td>\n",
       "      <td>0.617255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>400</td>\n",
       "      <td>0.530400</td>\n",
       "      <td>0.595580</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",
      "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",
      "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",
      "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",
      "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:257: 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=400, training_loss=0.7413255283981561, metrics={'train_runtime': 531.1137, 'train_samples_per_second': 1.506, 'train_steps_per_second': 0.753, 'total_flos': 2673745578436608.0, 'train_loss': 0.7413255283981561, 'epoch': 1.0})"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "270f9be9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-26T07:32:54.250807Z",
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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 0.59558\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:             eval/runtime 35.519\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:  eval/samples_per_second 5.631\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:    eval/steps_per_second 5.631\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:               total_flos 2673745578436608.0\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:              train/epoch 1\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:        train/global_step 400\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:          train/grad_norm 1.3515\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:      train/learning_rate 0\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:               train/loss 0.5304\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:               train_loss 0.74133\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:            train_runtime 531.1137\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: train_samples_per_second 1.506\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:   train_steps_per_second 0.753\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run \u001b[33mstellar-bush-7\u001b[0m at: \u001b[34m\u001b[4mhttps://wandb.ai/dyuva2005-vellore-institute-of-technology/Fine-tune%20Llama%203.2%20on%20Customer%20Support%20Dataset/runs/3n34i37m\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at: \u001b[34m\u001b[4mhttps://wandb.ai/dyuva2005-vellore-institute-of-technology/Fine-tune%20Llama%203.2%20on%20Customer%20Support%20Dataset\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-20241026_072103-3n34i37m/logs\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "wandb.finish()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "091e606b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-26T07:32:55.948445Z",
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     "shell.execute_reply": "2024-10-26T07:33:16.621175Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "I've taken note that you purchased the same item twice and would like to cancel order number {{Order Number}}. I understand the importance of resolving this situation for you. To proceed with the cancellation, could you please provide me with some additional details? For instance, what is the item's name, and do you have any other orders or concerns I can assist you with? Your satisfaction is my top priority, and I'm committed to making this process as smooth as possible for you.system\n"
     ]
    }
   ],
   "source": [
    "messages = [{\"role\": \"system\", \"content\": instruction},\n",
    "    {\"role\": \"user\", \"content\": \"I bought the same item twice, cancel order {{Order Number}}\"}]\n",
    "\n",
    "prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n",
    "    \n",
    "inputs = tokenizer(prompt, return_tensors='pt', padding=True, truncation=True).to(\"cuda\")\n",
    "\n",
    "outputs = model.generate(**inputs, max_new_tokens=150, num_return_sequences=1)\n",
    "\n",
    "text = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
    "\n",
    "print(text.split(\"assistant\")[1])"
   ]
  },
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     "text": [
      "/opt/conda/lib/python3.10/site-packages/peft/utils/save_and_load.py:257: UserWarning: Setting `save_embedding_layers` to `True` as the embedding layer has been resized during finetuning.\n",
      "  warnings.warn(\n"
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       "README.md:   0%|          | 0.00/5.17k [00:00<?, ?B/s]"
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       "adapter_model.safetensors:   0%|          | 0.00/1.67G [00:00<?, ?B/s]"
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      "text/plain": [
       "CommitInfo(commit_url='https://huggingface.co/yuva44/llama-3.2-3b-it-Ecommerce-ChatBot/commit/fcfccdaa1fc62ca9212777edb3f1d9dad9fa90da', commit_message='Upload model', commit_description='', oid='fcfccdaa1fc62ca9212777edb3f1d9dad9fa90da', pr_url=None, repo_url=RepoUrl('https://huggingface.co/yuva44/llama-3.2-3b-it-Ecommerce-ChatBot', endpoint='https://huggingface.co', repo_type='model', repo_id='yuva44/llama-3.2-3b-it-Ecommerce-ChatBot'), pr_revision=None, pr_num=None)"
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    "# Save the fine-tuned model\n",
    "trainer.model.save_pretrained(new_model)\n",
    "trainer.model.push_to_hub(new_model, use_temp_dir=False)"
   ]
  }
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