nlp-fine-tuning-base-llama-as-chat-instruct.ipynb

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   "source": [
    "!pip install -q accelerate -U\n",
    "!pip install -q bitsandbytes -U\n",
    "!pip install -q trl -U\n",
    "!pip install -q peft -U\n",
    "!pip install -q transformers -U"
   ]
  },
  {
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   "source": [
    "import os\n",
    "os.environ[\"WANDB_DISABLED\"] = \"true\""
   ]
  },
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       "version_minor": 0
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      "text/plain": [
       "Generating train_sft split:   0%|          | 0/207865 [00:00<?, ? examples/s]"
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    {
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      "text/plain": [
       "Generating test_sft split:   0%|          | 0/23110 [00:00<?, ? examples/s]"
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       "version_minor": 0
      },
      "text/plain": [
       "Generating train_gen split:   0%|          | 0/256032 [00:00<?, ? examples/s]"
      ]
     },
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    {
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      "text/plain": [
       "Generating test_gen split:   0%|          | 0/28304 [00:00<?, ? examples/s]"
      ]
     },
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   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "dataset = load_dataset(\"HuggingFaceH4/ultrachat_200k\", trust_remote_code=True, split=\"train_sft\")\n",
    "dataset = dataset.shuffle(seed=0).select(range(10_000))"
   ]
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
     ]
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      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
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     "metadata": {},
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    }
   ],
   "source": [
    "from transformers import AutoTokenizer\n",
    "\n",
    "#template_tokenizer = AutoTokenizer.from_pretrained(\"TinyLlama/TinyLlama-1.1B-Chat-v1.0\") \n",
    "# TinyLlama/TinyLlama-1.1B-Chat-v1.0: We use this Tokinizer from this chat intr just to use the \n",
    "# method apply_chat_template auto apply chat msg template\n",
    "\n",
    "# format_prompt Method use to convert in chat format\n",
    "#def format_prompt(example):\n",
    "#  \"\"\"Format the prompt using the <|user|> and <|assistant|> format\"\"\"\n",
    "#  chat = example['messages']\n",
    "#  prompt = template_tokenizer.apply_chat_template(chat, tokenize=False)\n",
    "\n",
    "#  return {'text': prompt}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8509c587",
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   },
   "outputs": [],
   "source": [
    "#print(format_prompt(dataset[0])['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c93ef4f1",
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   "source": [
    "#dataset = dataset.map(format_prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4b142b9f",
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   "outputs": [],
   "source": [
    "#print(dataset[0]['text'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e6af8b4",
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   "source": [
    "### Testing Base LLAMA Model\n",
    "- Let's see how base Tiny-LLAMA performs out of the box"
   ]
  },
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       "[{'generated_text': '\\nTell me something about Large Language Models\\n\\nA: I think the best way to understand LLMs is to look at the code.\\nThe code is available on GitHub, and you can find the code for the BERT model here.\\nThe code for the RoBERTa model is here.\\nThe code for the XLM model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the XLM-R model is here.\\nThe code for the X'}]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import pipeline\n",
    "\n",
    "# base model\n",
    "model_name = \"TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T\"\n",
    "\n",
    "pipe = pipeline(task='text-generation', model=model_name, device='cuda')\n",
    "\n",
    "\n",
    "# prompt\n",
    "# <|user|>, <|assistant|>\n",
    "\n",
    "prompt = \"\"\"<|user|>\n",
    "Tell me something about Large Language Models.</s>\n",
    "<|assistant|>\n",
    "\"\"\"\n",
    "\n",
    "prompt = \"\"\"\n",
    "Tell me something about Large Language Models\n",
    "\"\"\"\n",
    "\n",
    "output = pipe(prompt)\n",
    "output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bca37735",
   "metadata": {
    "papermill": {
     "duration": 0.006134,
     "end_time": "2024-11-27T15:08:35.418789",
     "exception": false,
     "start_time": "2024-11-27T15:08:35.412655",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Note: Base model can't provide an answer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a773813",
   "metadata": {
    "papermill": {
     "duration": 0.006061,
     "end_time": "2024-11-27T15:08:35.430832",
     "exception": false,
     "start_time": "2024-11-27T15:08:35.424771",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Model Configuration for Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f978db39",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-27T15:08:35.444649Z",
     "iopub.status.busy": "2024-11-27T15:08:35.443993Z",
     "iopub.status.idle": "2024-11-27T15:08:35.448856Z",
     "shell.execute_reply": "2024-11-27T15:08:35.447961Z"
    },
    "papermill": {
     "duration": 0.013633,
     "end_time": "2024-11-27T15:08:35.450443",
     "exception": false,
     "start_time": "2024-11-27T15:08:35.436810",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8fb41a15",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-27T15:08:35.463643Z",
     "iopub.status.busy": "2024-11-27T15:08:35.463377Z",
     "iopub.status.idle": "2024-11-27T15:08:35.468551Z",
     "shell.execute_reply": "2024-11-27T15:08:35.467701Z"
    },
    "papermill": {
     "duration": 0.013504,
     "end_time": "2024-11-27T15:08:35.470089",
     "exception": false,
     "start_time": "2024-11-27T15:08:35.456585",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# do the  4-bit quantization configuration in Q-LORA\n",
    "bnb_config = BitsAndBytesConfig(\n",
    "    load_in_4bit=True,\n",
    "    bnb_4bit_quant_type=\"nf4\",\n",
    "    bnb_4bit_compute_dtype='float16',\n",
    "    bnb_4bit_use_double_quant=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b8b42e8d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-27T15:08:35.483129Z",
     "iopub.status.busy": "2024-11-27T15:08:35.482840Z",
     "iopub.status.idle": "2024-11-27T15:08:35.689473Z",
     "shell.execute_reply": "2024-11-27T15:08:35.688521Z"
    },
    "papermill": {
     "duration": 0.215717,
     "end_time": "2024-11-27T15:08:35.691785",
     "exception": false,
     "start_time": "2024-11-27T15:08:35.476068",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
    "tokenizer.pad_token = \"<PAD>\"\n",
    "tokenizer.padding_size=\"left\"\n",
    "\n",
    "tokenizer.chat_template = \"\"\"\n",
    "{% for message in messages %}\n",
    "{% if message['role'] == 'user' %}\n",
    "<|user|>\\n{{ message['content'] }}</s>\n",
    "{% elif message['role'] == 'assistant' %}\n",
    "<|assistant|>\\n{{ message['content'] }}</s>\n",
    "{% endif %}\n",
    "{% endfor %}\n",
    "\"\"\".strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "017a3290",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-27T15:08:35.705742Z",
     "iopub.status.busy": "2024-11-27T15:08:35.705446Z",
     "iopub.status.idle": "2024-11-27T15:08:35.718859Z",
     "shell.execute_reply": "2024-11-27T15:08:35.717936Z"
    },
    "papermill": {
     "duration": 0.021946,
     "end_time": "2024-11-27T15:08:35.720488",
     "exception": false,
     "start_time": "2024-11-27T15:08:35.698542",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<|user|>\n",
      "3. Heat the vegetables in boiling water for the recommended time.</s>\n",
      "<|assistant|>\n",
      "I do not have information about the specific type of vegetables being referred to. However, here are general instructions for boiling most vegetables:\n",
      "\n",
      "1. Wash the vegetables thoroughly with clean water.\n",
      "2. Cut the vegetables into small or medium-sized pieces.\n",
      "3. Bring a pot of water to boil on the stove.\n",
      "4. Add a pinch of salt to the boiling water.\n",
      "5. Add the vegetables to the boiling water.\n",
      "6. Cook the vegetables for the recommended time (see cooking instructions on the package or look up cooking times for specific vegetables online).\n",
      "7. Test the vegetables for doneness using a fork or a knife. They should be tender but not overcooked and mushy.\n",
      "8. Once the vegetables are cooked to your desired tenderness, remove them from the boiling water using a slotted spoon or a strainer.\n",
      "9. Drain the vegetables and serve hot with your favorite seasonings or sauce.</s>\n",
      "<|user|>\n",
      "Can you add some information on how to prevent the vegetables from getting too soft or overcooked?</s>\n",
      "<|assistant|>\n",
      "Certainly, here are some tips to prevent vegetables from getting too soft or overcooked:\n",
      "\n",
      "1. Use fresh vegetables: Fresh vegetables require less cooking time and retain their texture and flavor better than older vegetables. 2. Cut vegetables into uniform-sized pieces: Cutting the vegetables into one-inch or uniform-sized pieces helps to ensure even cooking. 3. Use a timer: Set a timer according to the recommended cooking time for the vegetable you're cooking. Overcooking can cause vegetables to become mushy. 4. Taste test: Test the vegetables a few minutes before the recommended cooking time is over. If the vegetable is tender and cooked, remove it from the heat. 5. Use cold water to stop cooking: After cooking, drain the vegetable immediately and run it under cold water to stop the cooking process. 6. Avoid overcrowding the pot: Overcrowding can make the vegetables cook unevenly and take longer to cook. 7. Use steaming or blanching method: Try steaming or blanching instead of boiling to preserve the texture of the vegetables. With these tips, you should be able to cook vegetables that are firm, tender and full of flavor.</s>\n",
      "<|user|>\n",
      "Could you also give me some suggestions on what types of seasoning or sauce I could use to make my veggies taste better?</s>\n",
      "<|assistant|>\n",
      "Certainly! When it comes to seasoning or saucing vegetables, there are endless possibilities. Here are some ideas to make your vegetables taste better:\n",
      "\n",
      "1. Olive Oil and Lemon Juice: A classic Italian seasoning, pour some olive oil and a splash of lemon juice over your veggies for a zesty, tangy flavor. 2. Soy Sauce and Ginger: Mix soy sauce and grated ginger for an Asian inspired seasoning that'll give your vegetables a salty kick. 3. Garlic and Herbs: Mix minced garlic and fresh herbs, like parsley, rosemary or thyme, with olive oil and salt for a Mediterranean seasoning. 4. Balsamic Vinegar: Drizzle balsamic vinegar over your cooked vegetables for a rich, earthy flavor. 5. Parmesan Cheese: Shave some parmesan cheese over your veggies for a nutty, salty addition. 6. Pesto: Mix basil pesto with some olive oil and add to your vegetables for a flavorful, tangy kick. 7. Tahini Sauce: Mix tahini, lemon juice and garlic in a bowl, add some water for a creamy tahini sauce that complements any veggies. These are just a few seasoning ideas. Get creative and try your own spice combinations. Adding seasoning or sauce can make your vegetables taste delicious and exciting!</s>\n",
      "<|user|>\n",
      "These seasoning ideas are amazing! Can you give me some more ideas on how to cook veggies in a healthy and flavorful way?</s>\n",
      "<|assistant|>\n",
      "Of course, here are a few more ideas on how to cook your veggies in a healthy and flavorful way:\n",
      "\n",
      "1. Roasting: Roasting your vegetables is an easy and healthy way to bring out their natural flavors. Preheat your oven to 400F, toss your veggies with a little bit of olive oil, salt and pepper, and roast them for 20-30 minutes.\n",
      "\n",
      "2. Grilling: Grilling is a great way to cook vegetables during summer. Cut your veggies into large pieces, brush them with olive oil, season with salt and pepper, and grill them for a few minutes on each side.\n",
      "\n",
      "3. Stir-frying: Stir-frying is a quick and healthy way to cook vegetables. Heat some oil in a wok or a pan, add your chopped veggies, and stir-fry them for a few minutes until they're tender yet crisp. Add some soy sauce, ginger or garlic for extra flavor.\n",
      "\n",
      "4. Steaming: Steaming is a healthy cooking method that preserves the complete nutrition and texture. Place your veggies in a steamer basket, and steam them for 5-7 minutes, season them with herbs, butter or lemon juice.\n",
      "\n",
      "5. Sautéing: Sautéed vegetables can be a hearty side dish or served as the main course. Heat some butter or olive oil in a pan, add your chopped veggies and sauté them for a few minutes until they're tender yet crisp. Add some herbs, salt or spices for extra flavor.\n",
      "\n",
      "These are just a few cooking ideas. Regardless of how you cook your veggies, remember to use fresh produce, experiment different spices and enjoy their natural flavors!</s>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "example = dataset[0]\n",
    "chat = example['messages']\n",
    "prompt = tokenizer.apply_chat_template(chat, tokenize=False)\n",
    "print(prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c57bf8c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-27T15:08:35.734305Z",
     "iopub.status.busy": "2024-11-27T15:08:35.733558Z",
     "iopub.status.idle": "2024-11-27T15:08:38.798539Z",
     "shell.execute_reply": "2024-11-27T15:08:38.797789Z"
    },
    "papermill": {
     "duration": 3.073678,
     "end_time": "2024-11-27T15:08:38.800461",
     "exception": false,
     "start_time": "2024-11-27T15:08:35.726783",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    model_name,\n",
    "    device_map = \"auto\",\n",
    "    quantization_config=bnb_config\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a66c64f1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-27T15:08:38.814344Z",
     "iopub.status.busy": "2024-11-27T15:08:38.813996Z",
     "iopub.status.idle": "2024-11-27T15:08:38.817999Z",
     "shell.execute_reply": "2024-11-27T15:08:38.817172Z"
    },
    "papermill": {
     "duration": 0.01267,
     "end_time": "2024-11-27T15:08:38.819585",
     "exception": false,
     "start_time": "2024-11-27T15:08:38.806915",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model.config.use_cache=False\n",
    "model.config.pretraining_tp=1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2300a63e",
   "metadata": {
    "papermill": {
     "duration": 0.006089,
     "end_time": "2024-11-27T15:08:38.831837",
     "exception": false,
     "start_time": "2024-11-27T15:08:38.825748",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Prepare LoRA Configuration for PEFT Fine tuning\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "06724653",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-27T15:08:38.844933Z",
     "iopub.status.busy": "2024-11-27T15:08:38.844680Z",
     "iopub.status.idle": "2024-11-27T15:08:39.633041Z",
     "shell.execute_reply": "2024-11-27T15:08:39.632085Z"
    },
    "papermill": {
     "duration": 0.79726,
     "end_time": "2024-11-27T15:08:39.635161",
     "exception": false,
     "start_time": "2024-11-27T15:08:38.837901",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from peft import LoraConfig, prepare_model_for_kbit_training, get_peft_model\n",
    "\n",
    "peft_config = LoraConfig(\n",
    "    lora_alpha=32,\n",
    "    lora_dropout=0.1,\n",
    "    r=64,\n",
    "    bias='none',\n",
    "    task_type='CAUSAL_LM',\n",
    "    target_modules=['q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj']\n",
    ")\n",
    "\n",
    "model = prepare_model_for_kbit_training(model)\n",
    "\n",
    "model = get_peft_model(model, peft_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3f632eee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-27T15:08:39.649288Z",
     "iopub.status.busy": "2024-11-27T15:08:39.648932Z",
     "iopub.status.idle": "2024-11-27T15:08:39.655119Z",
     "shell.execute_reply": "2024-11-27T15:08:39.654315Z"
    },
    "papermill": {
     "duration": 0.015011,
     "end_time": "2024-11-27T15:08:39.656738",
     "exception": false,
     "start_time": "2024-11-27T15:08:39.641727",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(524288, 131072, 16384, 147456, 0.28125)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w = 2048*256\n",
    "a = 2048*64\n",
    "b = 64*256\n",
    "\n",
    "w, a, b, a+b, (a+b)/w"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbac6396",
   "metadata": {
    "papermill": {
     "duration": 0.006184,
     "end_time": "2024-11-27T15:08:39.669167",
     "exception": false,
     "start_time": "2024-11-27T15:08:39.662983",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Model Fine Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7d0d2797",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-27T15:08:39.682976Z",
     "iopub.status.busy": "2024-11-27T15:08:39.682694Z",
     "iopub.status.idle": "2024-11-27T17:06:06.247464Z",
     "shell.execute_reply": "2024-11-27T17:06:06.246673Z"
    },
    "papermill": {
     "duration": 7046.573782,
     "end_time": "2024-11-27T17:06:06.249136",
     "exception": false,
     "start_time": "2024-11-27T15:08:39.675354",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using the `WANDB_DISABLED` environment variable is deprecated and will be removed in v5. Use the --report_to flag to control the integrations used for logging result (for instance --report_to none).\n",
      "Using the `WANDB_DISABLED` environment variable is deprecated and will be removed in v5. Use the --report_to flag to control the integrations used for logging result (for instance --report_to none).\n",
      "/opt/conda/lib/python3.10/site-packages/huggingface_hub/utils/_deprecation.py:100: FutureWarning: Deprecated argument(s) used in '__init__': dataset_text_field, max_seq_length. 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/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": "d7230581e3c74c0ebaf430a9c57f472d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/10000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n",
      "  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='1250' max='1250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [1250/1250 1:57:04, 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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.600500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.416400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1.478800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.548600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>1.572200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>1.362500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>1.468700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>1.425400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>1.545100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>1.353100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>110</td>\n",
       "      <td>1.452900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>1.395300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>130</td>\n",
       "      <td>1.309800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>140</td>\n",
       "      <td>1.301300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150</td>\n",
       "      <td>1.356000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>160</td>\n",
       "      <td>1.389600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>170</td>\n",
       "      <td>1.459900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>180</td>\n",
       "      <td>1.401300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>190</td>\n",
       "      <td>1.395800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>200</td>\n",
       "      <td>1.425200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>210</td>\n",
       "      <td>1.466700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>220</td>\n",
       "      <td>1.474100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>230</td>\n",
       "      <td>1.334700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>240</td>\n",
       "      <td>1.403700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>250</td>\n",
       "      <td>1.377500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>260</td>\n",
       "      <td>1.344700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>270</td>\n",
       "      <td>1.369400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>280</td>\n",
       "      <td>1.421200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>290</td>\n",
       "      <td>1.413600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>300</td>\n",
       "      <td>1.248600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>310</td>\n",
       "      <td>1.306800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>320</td>\n",
       "      <td>1.397400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>330</td>\n",
       "      <td>1.400200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>340</td>\n",
       "      <td>1.456700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>350</td>\n",
       "      <td>1.267300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>360</td>\n",
       "      <td>1.471600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>370</td>\n",
       "      <td>1.353800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>380</td>\n",
       "      <td>1.387300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>390</td>\n",
       "      <td>1.304900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>400</td>\n",
       "      <td>1.399100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>410</td>\n",
       "      <td>1.292800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>420</td>\n",
       "      <td>1.411500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>430</td>\n",
       "      <td>1.369100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>440</td>\n",
       "      <td>1.407800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>450</td>\n",
       "      <td>1.329000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>460</td>\n",
       "      <td>1.406300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>470</td>\n",
       "      <td>1.360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>480</td>\n",
       "      <td>1.301200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>490</td>\n",
       "      <td>1.335700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>1.298400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>510</td>\n",
       "      <td>1.346100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>520</td>\n",
       "      <td>1.405600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>530</td>\n",
       "      <td>1.420900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>540</td>\n",
       "      <td>1.298300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>550</td>\n",
       "      <td>1.383300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>560</td>\n",
       "      <td>1.400400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>570</td>\n",
       "      <td>1.393200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>580</td>\n",
       "      <td>1.362700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>590</td>\n",
       "      <td>1.377200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>600</td>\n",
       "      <td>1.371500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>610</td>\n",
       "      <td>1.436800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>620</td>\n",
       "      <td>1.330800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>630</td>\n",
       "      <td>1.348100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>640</td>\n",
       "      <td>1.308500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>650</td>\n",
       "      <td>1.326500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>660</td>\n",
       "      <td>1.351400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>670</td>\n",
       "      <td>1.427000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>680</td>\n",
       "      <td>1.318900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>690</td>\n",
       "      <td>1.307800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>700</td>\n",
       "      <td>1.264100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>710</td>\n",
       "      <td>1.408600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>720</td>\n",
       "      <td>1.281200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>730</td>\n",
       "      <td>1.373700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>740</td>\n",
       "      <td>1.294200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>750</td>\n",
       "      <td>1.428800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>760</td>\n",
       "      <td>1.359500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>770</td>\n",
       "      <td>1.349500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>780</td>\n",
       "      <td>1.314700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>790</td>\n",
       "      <td>1.400300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>800</td>\n",
       "      <td>1.385500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>810</td>\n",
       "      <td>1.277800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>820</td>\n",
       "      <td>1.357100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>830</td>\n",
       "      <td>1.270500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>840</td>\n",
       "      <td>1.264800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>850</td>\n",
       "      <td>1.278300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>860</td>\n",
       "      <td>1.344200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>870</td>\n",
       "      <td>1.360100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>880</td>\n",
       "      <td>1.359900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>890</td>\n",
       "      <td>1.433300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>900</td>\n",
       "      <td>1.300600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>910</td>\n",
       "      <td>1.377300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>920</td>\n",
       "      <td>1.325600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>930</td>\n",
       "      <td>1.384200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>940</td>\n",
       "      <td>1.398200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>950</td>\n",
       "      <td>1.251900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>960</td>\n",
       "      <td>1.361900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>970</td>\n",
       "      <td>1.488000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>980</td>\n",
       "      <td>1.356000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>990</td>\n",
       "      <td>1.380000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>1.366300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1010</td>\n",
       "      <td>1.403700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1020</td>\n",
       "      <td>1.415500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1030</td>\n",
       "      <td>1.343500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1040</td>\n",
       "      <td>1.470100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1050</td>\n",
       "      <td>1.298500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1060</td>\n",
       "      <td>1.363500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1070</td>\n",
       "      <td>1.401100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1080</td>\n",
       "      <td>1.362000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1090</td>\n",
       "      <td>1.336200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1100</td>\n",
       "      <td>1.231200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1110</td>\n",
       "      <td>1.302400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1120</td>\n",
       "      <td>1.344500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1130</td>\n",
       "      <td>1.404800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1140</td>\n",
       "      <td>1.284400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1150</td>\n",
       "      <td>1.321200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1160</td>\n",
       "      <td>1.316800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1170</td>\n",
       "      <td>1.373000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1180</td>\n",
       "      <td>1.352100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1190</td>\n",
       "      <td>1.374300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1200</td>\n",
       "      <td>1.442700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1210</td>\n",
       "      <td>1.358800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1220</td>\n",
       "      <td>1.291900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1230</td>\n",
       "      <td>1.280500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1240</td>\n",
       "      <td>1.325500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1250</td>\n",
       "      <td>1.338600</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": [
      "/opt/conda/lib/python3.10/site-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n",
      "  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]\n",
      "/opt/conda/lib/python3.10/site-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n",
      "  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=1250, training_loss=1.3697360481262206, metrics={'train_runtime': 7030.1694, 'train_samples_per_second': 1.422, 'train_steps_per_second': 0.178, 'total_flos': 3.3318873700171776e+16, 'train_loss': 1.3697360481262206, 'epoch': 1.0})"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import TrainingArguments, Trainer\n",
    "from trl import SFTTrainer, SFTConfig\n",
    "\n",
    "output_dir = \"train_dir\"\n",
    "\n",
    "args = SFTConfig(\n",
    "    output_dir=output_dir,\n",
    "    per_device_train_batch_size=2,\n",
    "    gradient_accumulation_steps=4,\n",
    "    optim=\"paged_adamw_32bit\",\n",
    "    learning_rate=2e-4,\n",
    "    lr_scheduler_type=\"cosine\",\n",
    "    num_train_epochs=1,\n",
    "    logging_steps=10,\n",
    "    fp16=True,\n",
    "    gradient_checkpointing=True\n",
    ")\n",
    "\n",
    "trainer = SFTTrainer(\n",
    "    model=model,\n",
    "    train_dataset = dataset,\n",
    "    dataset_text_field='messages',\n",
    "    tokenizer = tokenizer,\n",
    "    args=args,\n",
    "    max_seq_length=512,\n",
    "    peft_config = peft_config\n",
    ")\n",
    "\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e43809cf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-27T17:06:06.265195Z",
     "iopub.status.busy": "2024-11-27T17:06:06.264653Z",
     "iopub.status.idle": "2024-11-27T17:06:06.762582Z",
     "shell.execute_reply": "2024-11-27T17:06:06.761674Z"
    },
    "papermill": {
     "duration": 0.50885,
     "end_time": "2024-11-27T17:06:06.765562",
     "exception": false,
     "start_time": "2024-11-27T17:06:06.256712",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "trainer.model.save_pretrained(\"TinyLlama-1.1B-qlora\")"
   ]
  },
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   "source": [
    "## Load Pre-Trained PEFT Model for Prediction"
   ]
  },
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   "source": [
    "from peft import AutoPeftModelForCausalLM\n",
    "\n",
    "model = AutoPeftModelForCausalLM.from_pretrained(\n",
    "    \"TinyLlama-1.1B-qlora\",\n",
    "    device_map='auto'\n",
    ")\n",
    "\n",
    "merged_model = model.merge_and_unload()"
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  },
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   "outputs": [
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     "text": [
      "<|user|>\n",
      "Tell me something about Large Language Models.</s>\n",
      "<|assistant|>\n",
      "Large Language Models (LLMs) are a type of artificial intelligence (AI) that can generate human-like language. They are trained on large amounts of text data, and they can be used to generate text in a variety of languages. LLMs are becoming increasingly popular in various fields, including natural language processing (NLP), machine translation, and chatbots.\n",
      "\n",
      "One of the key advantages of LLMs is their ability to generate human-like language. They can generate text that is grammatically correct, spelling correct, and has a natural flow and rhythm. This makes them ideal for use in chatbots, where they can provide helpful information or answer questions quickly and accurately.\n",
      "\n",
      "Another advantage of LLMs is their ability to learn from experience. They can be trained to generate different types of text based on the context of the input text. This means that they can generate different types of text based on the context of the input text, such as a question or a response. This can be useful for generating responses to questions or providing information in different contexts.\n",
      "\n",
      "Overall, LLMs are a powerful tool for generating human-like language, and they are becoming increasingly popular in various fields. They can be used to generate text in a variety of languages, and they can also be used to improve the quality and accuracy of chatbots.\n"
     ]
    }
   ],
   "source": [
    "from transformers import pipeline\n",
    "\n",
    "prompt = \"\"\"<|user|>\n",
    "Tell me something about Large Language Models.</s>\n",
    "<|assistant|>\n",
    "\"\"\"\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
    "tokenizer.pad_token = \"<PAD>\"\n",
    "tokenizer.padding_size=\"left\"\n",
    "\n",
    "pipe = pipeline(task='text-generation', model=merged_model, tokenizer=tokenizer)\n",
    "output = pipe(prompt)\n",
    "print(output[0]['generated_text'])"
   ]
  }
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