Google_Colab_Llama_Simple_Inference.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/TrelisResearch/install-guides/blob/main/Google_Colab_Llama_Simple_Inference.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# *Simple Llama Inference in Google Colab*\n",
        "---\n",
        "**Running in Google Colab**\n",
        "\n",
        "- Runtime -> Change runtime type.\n",
        "- Choose a GPU runtime (with at least a T4 GPU).\n",
        "- Run all cells.\n",
        "\n",
        "**Running on Laptop**\n",
        "\n",
        "Jupyter Tiny Llama for Laptop:\n",
        "- Save and re-load chats.\n",
        "- Upload pdf or text files for analysis.\n",
        "- Purchase access [here](https://buy.stripe.com/28ocNyf4pci78EMbJ9).\n",
        "\n",
        "---\n",
        "Prepared by Trelis Research.\n",
        "\n",
        "Find Trelis on [HuggingFace](https://huggingface.co/Trelis) and [YouTube](https://www.youtube.com/@TrelisResearch).\n",
        "\n"
      ],
      "metadata": {
        "id": "dDdy-k2BVDAo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### HuggingFace Login (optional)\n",
        "- You don't need this if you are using Trelis Function Calling Llama 2 7B, which is public.\n",
        "- You do need this to access private/gated repositories."
      ],
      "metadata": {
        "id": "vXJqCAr8_U2m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install huggingface_hub\n",
        "# from huggingface_hub import notebook_login\n",
        "\n",
        "# notebook_login()"
      ],
      "metadata": {
        "id": "8GB-sJruZ1ZS"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Google Drive Mounting (optional, but recommended)\n",
        "This saves you time the next time you load the model.\n",
        "\n",
        "If you don't use it, remove cache_dir from the model and tokeniser below."
      ],
      "metadata": {
        "id": "X-PAv_Oy1Pr2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cache_dir=''"
      ],
      "metadata": {
        "id": "Vxwy4Ct-jitJ"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')\n"
      ],
      "metadata": {
        "id": "oAzlPhWXVKXo"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# import os\n",
        "\n",
        "# # This is the path to the Google Drive folder.\n",
        "# drive_path = \"/content/drive\"\n",
        "\n",
        "# # This is the path where you want to store your cache.\n",
        "# cache_dir_path = os.path.join(drive_path, \"My Drive/huggingface_cache\")\n",
        "\n",
        "# # Check if the Google Drive folder exists. If it does, use it as the cache_dir.\n",
        "# # If not, set cache_dir to None to use the default Hugging Face cache location.\n",
        "# if os.path.exists(drive_path):\n",
        "#     cache_dir = cache_dir_path\n",
        "#     os.makedirs(cache_dir, exist_ok=True) # Ensure the directory exists\n",
        "# else:\n",
        "#     cache_dir = None"
      ],
      "metadata": {
        "id": "MH8ZqAEcVLTH"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "7wgbsa4U2TBk"
      },
      "outputs": [],
      "source": [
        "# https://stackoverflow.com/questions/56081324/why-are-google-colab-shell-commands-not-working\n",
        "import locale\n",
        "def getpreferredencoding(do_setlocale = True):\n",
        "    return \"UTF-8\"\n",
        "locale.getpreferredencoding = getpreferredencoding"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TzGa3YSCgl4C"
      },
      "source": [
        "# Setup and Install\n",
        "- It's best to run Llama models on a GPU, which you can do using a free Colab notebook.\n",
        "- Check the Google Colab runtime to the top right corner.\n",
        "- Or, go to the menu -> Runtime -> Change Runtime Type.\n",
        "- Select GPU (T4)."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the runtime to cpu or gpu. fLlama 7B (or 13B) requires too much RAM to work on cpu alone on a free or PRO Colab notebook - so use runtime = \"gpu\".\n",
        "runtime = \"gpu\"  # OR \"cpu\"\n",
        "\n",
        "if runtime == \"cpu\":\n",
        "    runtimeFlag = \"cpu\"\n",
        "elif runtime == \"gpu\":\n",
        "    runtimeFlag = \"cuda:0\"\n",
        "else:\n",
        "    print(\"Invalid runtime. Please set it to either 'cpu' or 'gpu'.\")\n",
        "    runtimeFlag = None\n",
        "\n",
        "print(\"Runtime flag is:\", runtimeFlag)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RplPiN-K0-Gb",
        "outputId": "8d55a376-f701-469c-b818-2f36bb107935"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Runtime flag is: cuda:0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### DEFINE THE HUGGING SPACE MODEL\n",
        "\n",
        "# # 1.1B model\n",
        "model_id = \"PY007/TinyLlama-1.1B-Chat-v0.1\"\n",
        "\n",
        "# # 7B model\n",
        "# model_id = \"Trelis/Llama-2-7b-chat-hf-function-calling-v2\"\n",
        "# model_id = \"meta-llama/Llama-2-7b-chat-hf\" # alternately, you can load this and then apply the adapter (see the commented out cells below).\n",
        "\n",
        "# 13B model (recommended for better function prompting precision, required paid access)\n",
        "# model_id = \"Trelis/Llama-2-13b-chat-hf-function-calling-v2\""
      ],
      "metadata": {
        "id": "NCHZB6G2PIDM"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Install"
      ],
      "metadata": {
        "id": "qWc_szlJP-Gx"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WGeTgx1Hgljb",
        "outputId": "c7bb1299-c050-466b-9464-2cf425c1a5f2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.6/92.6 MB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m295.0/295.0 kB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.8/3.8 MB\u001b[0m \u001b[31m13.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m22.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m19.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for accelerate (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.2/42.2 kB\u001b[0m \u001b[31m1.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.6/211.6 MB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q -U bitsandbytes\n",
        "!pip install -q -U git+https://github.com/huggingface/transformers.git\n",
        "!pip install -q -U git+https://github.com/huggingface/accelerate.git\n",
        "!pip install -q -U einops\n",
        "!pip install -q -U safetensors\n",
        "!pip install -q -U torch\n",
        "!pip install -q -U xformers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p1zodOgcgqnO"
      },
      "source": [
        "### Import"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "v_r4SL_KmHGs"
      },
      "outputs": [],
      "source": [
        "import transformers\n",
        "import torch\n",
        "import json\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, pipeline, TextStreamer"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load Model\n",
        "This can take 30 mins, which is why connecting Google Drive for caching is recommended. The next time you run, it will be much faster because your model will only need to load checkpoint shards rather than the full model from HuggingFace."
      ],
      "metadata": {
        "id": "alLhGWu9Ngvg"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "tz4eBuxZHi7F",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
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        "outputId": "dc944cab-7aca-44ab-fe30-d5f89b1efa4c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/652 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "04d019786e0242f19b6343876515be38"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/4.40G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/63.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "027213e296094c8bb9d5184f5159157f"
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          },
          "metadata": {}
        }
      ],
      "source": [
        "if runtime == \"gpu\":\n",
        "    # Load the model in 4-bit to allow it to fit in a free Google Colab runtime with a CPU and T4 GPU\n",
        "    bnb_config = BitsAndBytesConfig(\n",
        "        load_in_4bit=True,\n",
        "        bnb_4bit_use_double_quant=True, #adds speed with minimal loss of quality.\n",
        "        bnb_4bit_quant_type=\"nf4\",\n",
        "        bnb_4bit_compute_dtype=torch.bfloat16,\n",
        "    )\n",
        "    model = AutoModelForCausalLM.from_pretrained(\n",
        "        model_id,\n",
        "        quantization_config=bnb_config,\n",
        "        # device_map='auto', # for inference use 'auto', for training use device_map={\"\":0}\n",
        "        device_map=runtimeFlag,\n",
        "        trust_remote_code=True,\n",
        "        # rope_scaling = {\"type\": \"dynamic\", \"factor\": 2.0}, # allows for a max sequence length of 8192 tokens !!! [not tested in this notebook yet]\n",
        "        cache_dir=cache_dir)\n",
        "    # Not possible to use bits and bits if using cpu only, afaik\n",
        "else:\n",
        "    model = AutoModelForCausalLM.from_pretrained(model_id, device_map=runtimeFlag, trust_remote_code=True, cache_dir=cache_dir) # this can easily exhaust Colab RAM. Note that bfloat16 can't be used on cpu."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Add an adapter\n",
        "This is a more complicated but usually faster way to get the model loaded."
      ],
      "metadata": {
        "id": "IdoU3ZEv2Vfn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install -q -U git+https://github.com/huggingface/peft.git\n",
        "\n",
        "# from peft import PeftModel\n",
        "\n",
        "# adapter_model = \"Trelis/Llama-2-7b-chat-hf-function-calling-adapters-v2\"\n",
        "\n",
        "# # load perf model with new adapters\n",
        "# model = PeftModel.from_pretrained(\n",
        "#     model,\n",
        "#     adapter_model,\n",
        "# )"
      ],
      "metadata": {
        "id": "81k_g1Yy2VBi"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Set up the Tokenizer"
      ],
      "metadata": {
        "id": "N_bkc-1aQUB7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id, cache_dir=cache_dir, use_fast=True) # will use the Rust fast tokenizer if available\n",
        "\n",
        "# from transformers import LlamaTokenizer\n",
        "# tokenizer = LlamaTokenizer.from_pretrained(model_id, cache_dir=cache_dir) # will use the Rust fast tokenizer if available"
      ],
      "metadata": {
        "id": "kByEN730YOh7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232,
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            "Loading the tokenizer from the `special_tokens_map.json` and the `added_tokens.json` will be removed in `transformers 5`,  it is kept for forward compatibility, but it is recommended to update your `tokenizer_config.json` by uploading it again. You will see the new `added_tokens_decoder` attribute that will store the relevant information.\n",
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        "# Inference (Simple stream)\n",
        "\n"
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        "def stream(user_prompt):\n",
        "    system_prompt = 'Assistant responses are concise.'\n",
        "\n",
        "    # Guanaco style for TinyLlama\n",
        "    B_INST, E_INST = \"### Human:\", \"### Assistant:\"\n",
        "    B_SYS, E_SYS = \"\\n\", \"\\n\\n\"\n",
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        "    # # Llama style\n",
        "    # B_INST, E_INST = \"[INST]\", \"[/INST]\"\n",
        "    # B_SYS, E_SYS = \"<<SYS>>\\n\", \"\\n<</SYS>>\\n\\n\"\n",
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            "<s> ### Human: \n",
            "Assistant responses are concise.\n",
            "\n",
            "List the planets our solar system ### Assistant:\n",
            "\n",
            "1. Mercury\n",
            "2. Venus\n",
            "3. Earth\n",
            "4. Mars\n",
            "5. Jupiter\n",
            "6. Saturn\n",
            "7. Uranus\n",
            "8. Neptune\n",
            "\n",
            "\n",
            "\n",
            "Concise and to the point\n"
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