Aurelius_inference.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "-q3yi4RLV4pJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "51fad6d7-4bc7-4fe2-dfd7-525ed7a6a00f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Successfully installed datasets-2.18.0 dill-0.3.8 multiprocess-0.70.16\n",
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            "Installing collected packages: peft\n",
            "Successfully installed peft-0.9.0\n"
          ]
        }
      ],
      "source": [
        "!pip install accelerate\n",
        "!pip install bitsandbytes\n",
        "!pip install gradio\n",
        "!pip install protobuf\n",
        "!pip install scipy\n",
        "!pip install sentencepiece\n",
        "!pip install torch\n",
        "!pip install transformers\n",
        "!pip install datasets\n",
        "!pip install peft"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "qXbTqkvzYJ2s"
      },
      "outputs": [],
      "source": [
        "import argparse\n",
        "import bitsandbytes as bnb\n",
        "from datasets import load_dataset\n",
        "from functools import partial\n",
        "import os\n",
        "from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training, AutoPeftModelForCausalLM\n",
        "import torch\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed, Trainer, TrainingArguments, BitsAndBytesConfig, \\\n",
        "    DataCollatorForLanguageModeling, Trainer, TrainingArguments\n",
        "from datasets import load_dataset\n",
        "from torch import cuda, bfloat16\n",
        "import transformers\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "CGYqyzy8YT29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "c13c68cc10e34517ae302eebcaea2d1f",
            "fbe47c25dcc146619b3a161434ef48cc",
            "7e04d2b0e3b341048b1deb421d398c44",
            "710a5d9e189f4d22be8f4ccf1febbcea",
            "ea32bce123234855bc05f1944a897a0d",
            "c9e408fa17da4489b1bf985e9b66ce94",
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            "2a7e5e2c0af246668fcdf17c6a5fe123",
            "b2c505db455342608d498c69135453c6",
            "68ff77a0496641c9aee51e6a5e975a61",
            "ad33616de54f46788e6b0dab5a4eced7",
            "fbac035a67664b7c98942ae3c3494a16",
            "f684ab4f751e40dbbf4142254d380a63",
            "ba79cd2ef0f84daa8de7655382deb105",
            "8c5e5a7ece1c47bfac549d3228a68cbe",
            "a6685608d5f34913a59b13ba8d2a2b78",
            "7e4f53db79a8481cbd7b789b40b3149b",
            "718292fafc104ef585ce63aeab653b93",
            "8ee89c6db72946848a852f37960cfcbf",
            "d51c7421be8a42f58a36ea0b84777427",
            "b63c81de136a4826b790b29d9dd0be69",
            "3006ce381f994c7e8d9d4ae49b4a84f3",
            "5b7f361b91c64c94935019e83a9a88a3",
            "78bc513899bc4248a922f79345b573d4",
            "d924299cf3064889bb1a9fc050030616",
            "1f3e667bb78846b684b71f5a0374f5af",
            "ebaffbf86e184e2bb4f74afbb2bfbaf4",
            "b5b02749dc864419bd2cdaa084fd7849",
            "e35bbc0f33a740cda537ab41eb64d652",
            "0e8c0fa3f71f4934b8e882cb193f749f",
            "b3a1889a8d7e49b5b847acf814a23277",
            "de8c6e029a034acbbb1a86a56e491c6e"
          ]
        },
        "outputId": "0974b3b2-3b72-48a6-b0cb-3a9d7633c7ca"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c13c68cc10e34517ae302eebcaea2d1f"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "YcvtudTlWxic",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578,
          "referenced_widgets": [
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            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
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          "text": [
            "The `load_in_4bit` and `load_in_8bit` arguments are deprecated and will be removed in the future versions. Please, pass a `BitsAndBytesConfig` object in `quantization_config` argument instead.\n"
          ]
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      "source": [
        "import torch\n",
        "from peft import PeftModel, PeftConfig\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "\n",
        "peft_model_id = \"anshulsc/Aurelius\"\n",
        "config = PeftConfig.from_pretrained(peft_model_id)\n",
        "model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto')\n",
        "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
        "\n",
        "# Load the Lora model\n",
        "model = PeftModel.from_pretrained(model, peft_model_id)"
      ]
    },
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      "execution_count": 13,
      "metadata": {
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        },
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          "text": [
            "nsturction : Wirte code for Fibonnaci seires\n",
            "  ### code: \n",
            "  ```\n",
            "    def fibonacci(n):\n",
            "        if n <= 1:\n",
            "            return n\n",
            "        else:\n",
            "            return fibonacci(n-1) + fibonacci(n-2)\n",
            "    ```\n",
            "  \n",
            "  This code defines a function `fibonacci` that takes an integer `n` as input and returns the `n`th Fibonacci number. The function uses a recursive approach, where it calls itself with the arguments `n-1` and `n-2` until it reaches the base case where `n` is equal to or less than 1, at which point it returns `n`.\n",
            "  \n",
            "  \n",
            "  ### Examples:\n",
            "  \n",
            "  \n",
            "  ```\n",
            "    print(fibonacci(5)) # Output: 5\n",
            "    print(fibonacci(8)) # Output: 21\n",
            "    print(fibonacci(13)) # Output:\n"
          ]
        }
      ],
      "source": [
        "# @title Chat with Avalon\n",
        "question_to_ask = \"Wirte code for Fibonnaci seires\" # @param {type:\"string\"}\n",
        "\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "def get_answer(txt):\n",
        "  input_instruction = f'''\n",
        "\n",
        "  ### Insturction : {txt}\n",
        "  ### code:\n",
        "  ```\n",
        "   '''\n",
        "\n",
        "  batch = tokenizer(input_instruction, return_tensors='pt')\n",
        "  batch.to(device)\n",
        "  with torch.cuda.amp.autocast():\n",
        "    output_tokens = model.generate(**batch, max_new_tokens=200)\n",
        "  output = tokenizer.decode(output_tokens[0], skip_special_tokens=True)\n",
        "  output_response = output[output.rfind(\"###Response:\") + len(\"###Response:\"):].strip()\n",
        "  return(output_response)\n",
        "\n",
        "print(get_answer(question_to_ask))"
      ]
    },
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