Upload sft_phi3.ipynb with huggingface_hub

sft_phi3.ipynb

@@ -0,0 +1,496 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "578786b8-092a-4de8-9955-4e87da557639",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: peft in /opt/conda/lib/python3.10/site-packages (0.11.1)\n",
+      "Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from peft) (1.26.3)\n",
+      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from peft) (23.1)\n",
+      "Requirement already satisfied: psutil in /opt/conda/lib/python3.10/site-packages (from peft) (5.9.0)\n",
+      "Requirement already satisfied: pyyaml in /opt/conda/lib/python3.10/site-packages (from peft) (6.0.1)\n",
+      "Requirement already satisfied: torch>=1.13.0 in /opt/conda/lib/python3.10/site-packages (from peft) (2.2.0)\n",
+      "Requirement already satisfied: transformers in /opt/conda/lib/python3.10/site-packages (from peft) (4.42.3)\n",
+      "Requirement already satisfied: tqdm in /opt/conda/lib/python3.10/site-packages (from peft) (4.66.4)\n",
+      "Requirement already satisfied: accelerate>=0.21.0 in /opt/conda/lib/python3.10/site-packages (from peft) (0.32.0)\n",
+      "Requirement already satisfied: safetensors in /opt/conda/lib/python3.10/site-packages (from peft) (0.4.3)\n",
+      "Requirement already satisfied: huggingface-hub>=0.17.0 in /opt/conda/lib/python3.10/site-packages (from peft) (0.23.4)\n",
+      "Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.17.0->peft) (3.13.1)\n",
+      "Requirement already satisfied: fsspec>=2023.5.0 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.17.0->peft) (2023.12.2)\n",
+      "Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.17.0->peft) (2.32.3)\n",
+      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.17.0->peft) (4.9.0)\n",
+      "Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch>=1.13.0->peft) (1.12)\n",
+      "Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch>=1.13.0->peft) (3.1)\n",
+      "Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch>=1.13.0->peft) (3.1.2)\n",
+      "Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.10/site-packages (from transformers->peft) (2024.5.15)\n",
+      "Requirement already satisfied: tokenizers<0.20,>=0.19 in /opt/conda/lib/python3.10/site-packages (from transformers->peft) (0.19.1)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch>=1.13.0->peft) (2.1.3)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub>=0.17.0->peft) (2.0.4)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub>=0.17.0->peft) (3.4)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub>=0.17.0->peft) (1.26.18)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub>=0.17.0->peft) (2023.11.17)\n",
+      "Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch>=1.13.0->peft) (1.3.0)\n",
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
+      "\u001b[0m"
+     ]
+    }
+   ],
+   "source": [
+    "#!pip install huggingface_hub torch transformers datasets trl \n",
+    "#!pip install flash-attn --no-build-isolation\n",
+    "!pip install --upgrade peft"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4a74bec4-4bf0-47be-802a-046073da573e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import logging\n",
+    "\n",
+    "import datasets\n",
+    "from datasets import load_dataset\n",
+    "from peft import LoraConfig\n",
+    "import torch\n",
+    "import transformers\n",
+    "from trl import SFTTrainer, SFTConfig\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, BitsAndBytesConfig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8a9bc6f8-4a1e-42d8-897d-5225e1b5011a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_id = (\"wikitext\", \"wikitext-103-raw-v1\")\n",
+    "dataset_id = \"HuggingFaceH4/ultrachat_200k\"\n",
+    "\n",
+    "dataset = load_dataset(dataset_id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f3b226eb-b159-4533-bd33-2746181a80b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "training_config = {\n",
+    "    \"bf16\": True,\n",
+    "    \"do_eval\": False,\n",
+    "    \"do_train\": True, # defualts to False, not sure where this fits\n",
+    "    \"learning_rate\": 5.0e-06,\n",
+    "    \"log_level\": \"info\",\n",
+    "    \"logging_steps\": 20,\n",
+    "    \"logging_strategy\": \"steps\",\n",
+    "    \"lr_scheduler_type\": \"cosine\",\n",
+    "    \"num_train_epochs\": 1,\n",
+    "    \"max_steps\": -1,\n",
+    "    \"output_dir\": \"./checkpoint_dir\", # model predictions and checkpoints\n",
+    "    \"overwrite_output_dir\": True,\n",
+    "    \"per_device_eval_batch_size\": 4,\n",
+    "    \"per_device_train_batch_size\": 4,\n",
+    "    \"remove_unused_columns\": True,\n",
+    "    \"save_steps\": 100,\n",
+    "    \"save_total_limit\": 1,\n",
+    "    \"seed\": 0,\n",
+    "    \"gradient_checkpointing\": True,\n",
+    "    \"gradient_checkpointing_kwargs\":{\"use_reentrant\": False},\n",
+    "    \"gradient_accumulation_steps\": 1, # number of steps to accumulate before beckprop\n",
+    "    \"warmup_ratio\": 0.2,\n",
+    "    \"packing\": False,\n",
+    "    \"max_seq_length\": 2048,\n",
+    "    \"dataset_text_field\": \"text\",\n",
+    "    }\n",
+    "\n",
+    "peft_config = {\n",
+    "    \"r\": 16, # default values VV\n",
+    "    \"lora_alpha\": 32,\n",
+    "    \"lora_dropout\": 0.05,\n",
+    "    \"bias\": \"none\",\n",
+    "    \"task_type\": \"CAUSAL_LM\",\n",
+    "    \"target_modules\": \"all-linear\",\n",
+    "    \"modules_to_save\": None,\n",
+    "}\n",
+    "\n",
+    "train_conf = SFTConfig(**training_config)\n",
+    "#train_conf = TrainingArguments(**training_config)\n",
+    "peft_conf = LoraConfig(**peft_config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "20c9d834-50fe-4495-b003-7d80495c8439",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "08aed232727444ab814beb2c188090eb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Exception ignored in: <bound method IPythonKernel._clean_thread_parent_frames of <ipykernel.ipkernel.IPythonKernel object at 0x76c288d725c0>>\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/ipykernel/ipkernel.py\", line 775, in _clean_thread_parent_frames\n",
+      "    def _clean_thread_parent_frames(\n",
+      "KeyboardInterrupt: \n",
+      "Exception ignored in: <bound method IPythonKernel._clean_thread_parent_frames of <ipykernel.ipkernel.IPythonKernel object at 0x76c288d725c0>>\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/ipykernel/ipkernel.py\", line 775, in _clean_thread_parent_frames\n",
+      "    def _clean_thread_parent_frames(\n",
+      "KeyboardInterrupt: \n",
+      "Exception ignored in: <bound method IPythonKernel._clean_thread_parent_frames of <ipykernel.ipkernel.IPythonKernel object at 0x76c288d725c0>>\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/ipykernel/ipkernel.py\", line 775, in _clean_thread_parent_frames\n",
+      "    def _clean_thread_parent_frames(\n",
+      "KeyboardInterrupt: \n",
+      "Exception ignored in: <bound method IPythonKernel._clean_thread_parent_frames of <ipykernel.ipkernel.IPythonKernel object at 0x76c288d725c0>>\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/opt/conda/lib/python3.10/site-packages/ipykernel/ipkernel.py\", line 775, in _clean_thread_parent_frames\n",
+      "    def _clean_thread_parent_frames(\n",
+      "KeyboardInterrupt: \n"
+     ]
+    }
+   ],
+   "source": [
+    "checkpoint_path = \"microsoft/Phi-3-mini-128k-instruct\"\n",
+    "model_kwargs = dict(\n",
+    "    use_cache=False,\n",
+    "    trust_remote_code=True,\n",
+    "    attn_implementation=\"flash_attention_2\",  # loading the model with flash-attenstion support\n",
+    "    torch_dtype=torch.bfloat16,\n",
+    "    device_map=\"auto\"\n",
+    ")\n",
+    "\n",
+    "model = AutoModelForCausalLM.from_pretrained(checkpoint_path, **model_kwargs)\n",
+    "tokenizer = AutoTokenizer.from_pretrained(checkpoint_path, truncation=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d684252c-2151-4601-8ebb-398bd3a63f00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tokenizer.model_max_length = 2048\n",
+    "#tokenizer.pad_token = tokenizer.unk_token  # use unk rather than eos token to prevent endless generation\n",
+    "#tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)\n",
+    "tokenizer.padding_side = 'right'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "75869100-99f7-49c7-a9d3-7a3950dd7d72",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "def preproc(examples, tokenizer):\n",
+    "    messages = examples['messages']\n",
+    "    examples['text'] = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False) #, return_dict=True)\n",
+    "    return examples\n",
+    "\n",
+    "train_dataset = dataset['train_sft']\n",
+    "test_dataset = dataset['test_sft']\n",
+    "\n",
+    "train_dataset = train_dataset.map(preproc,\n",
+    "                            fn_kwargs={'tokenizer':tokenizer},\n",
+    "                            num_proc=24,\n",
+    "                            #batched=True,\n",
+    "                            remove_columns=list(train_dataset.features)).select(range(1000))\n",
+    "\n",
+    "test_dataset = test_dataset.map(preproc,\n",
+    "                            fn_kwargs={'tokenizer':tokenizer},\n",
+    "                            num_proc=24,\n",
+    "                            #batched=True,\n",
+    "                            remove_columns=list(test_dataset.features))#[10000:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "56cd1b31-6f7e-4c7d-8524-b12cf94b9c9f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d79f04152484f9494e389b264fc7176",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using auto half precision backend\n"
+     ]
+    }
+   ],
+   "source": [
+    "trainer = SFTTrainer(\n",
+    "    model=model,\n",
+    "    args=train_conf,\n",
+    "    peft_config=peft_conf,\n",
+    "    train_dataset=train_dataset,\n",
+    "    #eval_dataset=test_dataset,\n",
+    "    # max_seq_length=tokenizer.model_max_length,\n",
+    "    # dataset_text_field=\"text\",\n",
+    "    tokenizer=tokenizer,\n",
+    "    # packing=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "d8e6b669-1717-429a-9c43-3c02adb8a3d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "***** Running training *****\n",
+      "  Num examples = 1,000\n",
+      "  Num Epochs = 1\n",
+      "  Instantaneous batch size per device = 4\n",
+      "  Total train batch size (w. parallel, distributed & accumulation) = 4\n",
+      "  Gradient Accumulation steps = 1\n",
+      "  Total optimization steps = 250\n",
+      "  Number of trainable parameters = 25,165,824\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      \n",
+       "      <progress value='4' max='250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      [  4/250 00:04 < 09:17, 0.44 it/s, Epoch 0.01/1]\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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table><p>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[16], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m train_result \u001b[38;5;241m=\u001b[39m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m metrics \u001b[38;5;241m=\u001b[39m train_result\u001b[38;5;241m.\u001b[39mmetrics\n\u001b[1;32m      3\u001b[0m trainer\u001b[38;5;241m.\u001b[39msave_state()\n",
+      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/trl/trainer/sft_trainer.py:440\u001b[0m, in \u001b[0;36mSFTTrainer.train\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mneftune_noise_alpha \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_trainer_supports_neftune:\n\u001b[1;32m    438\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_trl_activate_neftune(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel)\n\u001b[0;32m--> 440\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    442\u001b[0m \u001b[38;5;66;03m# After training we make sure to retrieve back the original forward pass method\u001b[39;00m\n\u001b[1;32m    443\u001b[0m \u001b[38;5;66;03m# for the embedding layer by removing the forward post hook.\u001b[39;00m\n\u001b[1;32m    444\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mneftune_noise_alpha \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_trainer_supports_neftune:\n",
+      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/transformers/trainer.py:1932\u001b[0m, in \u001b[0;36mTrainer.train\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m   1930\u001b[0m         hf_hub_utils\u001b[38;5;241m.\u001b[39menable_progress_bars()\n\u001b[1;32m   1931\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1932\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minner_training_loop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1933\u001b[0m \u001b[43m        \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1934\u001b[0m \u001b[43m        \u001b[49m\u001b[43mresume_from_checkpoint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresume_from_checkpoint\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1935\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtrial\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrial\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1936\u001b[0m \u001b[43m        \u001b[49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1937\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/transformers/trainer.py:2268\u001b[0m, in \u001b[0;36mTrainer._inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   2265\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallback_handler\u001b[38;5;241m.\u001b[39mon_step_begin(args, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol)\n\u001b[1;32m   2267\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maccelerator\u001b[38;5;241m.\u001b[39maccumulate(model):\n\u001b[0;32m-> 2268\u001b[0m     tr_loss_step \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2270\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m   2271\u001b[0m     args\u001b[38;5;241m.\u001b[39mlogging_nan_inf_filter\n\u001b[1;32m   2272\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_torch_xla_available()\n\u001b[1;32m   2273\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m (torch\u001b[38;5;241m.\u001b[39misnan(tr_loss_step) \u001b[38;5;129;01mor\u001b[39;00m torch\u001b[38;5;241m.\u001b[39misinf(tr_loss_step))\n\u001b[1;32m   2274\u001b[0m ):\n\u001b[1;32m   2275\u001b[0m     \u001b[38;5;66;03m# if loss is nan or inf simply add the average of previous logged losses\u001b[39;00m\n\u001b[1;32m   2276\u001b[0m     tr_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m tr_loss \u001b[38;5;241m/\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mglobal_step \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_globalstep_last_logged)\n",
+      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/transformers/trainer.py:3324\u001b[0m, in \u001b[0;36mTrainer.training_step\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m   3322\u001b[0m         scaled_loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[1;32m   3323\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 3324\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maccelerator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloss\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3326\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\u001b[38;5;241m.\u001b[39mdetach() \u001b[38;5;241m/\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39mgradient_accumulation_steps\n",
+      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/accelerate/accelerator.py:2151\u001b[0m, in \u001b[0;36mAccelerator.backward\u001b[0;34m(self, loss, **kwargs)\u001b[0m\n\u001b[1;32m   2149\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlomo_backward(loss, learning_rate)\n\u001b[1;32m   2150\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2151\u001b[0m     \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/_tensor.py:522\u001b[0m, in \u001b[0;36mTensor.backward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    512\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    513\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m    514\u001b[0m         Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[1;32m    515\u001b[0m         (\u001b[38;5;28mself\u001b[39m,),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    520\u001b[0m         inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[1;32m    521\u001b[0m     )\n\u001b[0;32m--> 522\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    523\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\n\u001b[1;32m    524\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/autograd/__init__.py:266\u001b[0m, in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    261\u001b[0m     retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[1;32m    263\u001b[0m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[1;32m    264\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[1;32m    265\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[0;32m--> 266\u001b[0m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[1;32m    267\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    268\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    269\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    270\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    271\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    272\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    273\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    274\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "train_result = trainer.train()\n",
+    "metrics = train_result.metrics\n",
+    "trainer.save_state()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "4d4207fc-1578-4591-a480-467fd2a5855b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'train_runtime': 506.2204,\n",
+       " 'train_samples_per_second': 1.975,\n",
+       " 'train_steps_per_second': 0.494,\n",
+       " 'total_flos': 4.041582948790272e+16,\n",
+       " 'train_loss': 1.1037534561157227,\n",
+       " 'epoch': 1.0}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f92339ec-0448-40d2-9458-6242e35b9bdc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from peft import PeftConfig, PeftModel \n",
+    "\n",
+    "checkpoint_path = \"microsoft/Phi-3-mini-128k-instruct\"\n",
+    "adapter_path = \"./checkpoint_dir/checkpoint-250\"\n",
+    "\n",
+    "model_kwargs = dict(\n",
+    "    use_cache=False,\n",
+    "    trust_remote_code=True,\n",
+    "    attn_implementation=\"flash_attention_2\",  # loading the model with flash-attenstion support\n",
+    "    torch_dtype=torch.bfloat16,\n",
+    "    device_map=\"auto\"\n",
+    ")\n",
+    "\n",
+    "model = AutoModelForCausalLM.from_pretrained(checkpoint_path, **model_kwargs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f0cf458d-8b4f-4ff9-bd60-bbe510416cea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = PeftModel.from_pretrained(model, adapter_path)\n",
+    "tokenizer = AutoTokenizer.from_pretrained(checkpoint_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "b5ada882-b7d2-46c5-ba5b-54fab2556832",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_text = [\n",
+    "    {'role': 'user', 'content': \"Tell me about cats\"},\n",
+    "]\n",
+    "\n",
+    "input = \"Tell me about cats\"\n",
+    "\n",
+    "input = tokenizer(input, return_tensors='pt')\n",
+    "\n",
+    "output = model.generate(\n",
+    "    input['input_ids'].to('cuda'),\n",
+    "    max_length=50,  # Maximum length of the generated text\n",
+    "    num_return_sequences=1,  # Number of sequences to generate\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "139e9973-003a-484f-95f8-42428dd436f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tell me about cats.\n",
+      "\n",
+      "Chatbot: Cats are fascinating creatures! They are known for their agility, independence, and unique behaviors. They have a keen sense of hearing and can see well in low light\n"
+     ]
+    }
+   ],
+   "source": [
+    "generated_text = tokenizer.decode(output[0], skip_special_tokens=True)\n",
+    "\n",
+    "print(generated_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6dc4ddb3-3cbf-4d6e-9f57-45acb8acbe25",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}