llama2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HLdoj4cz-xal"
      },
      "source": [
        "# Llama2.py\n",
        "\n",
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/tairov/llama2.py/blob/master/llama2.ipynb)\n",
        "\n",
        "More details can be found in the [README.md](README.md) ."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Une3Ozlnu1B7"
      },
      "outputs": [],
      "source": [
        "#@title Clone Project\n",
        "\n",
        "!git clone https://github.com/tairov/llama2.py.git\n",
        "%cd llama2.py"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "thm0ZBrtSgoC"
      },
      "outputs": [],
      "source": [
        "#@title Pick Your Model\n",
        "\n",
        "#@markdown Choose model\n",
        "model = \"stories15M\" #@param [\"stories15M\", \"stories42M\", \"stories110M\"]\n",
        "\n",
        "download_url = \"\"\n",
        "\n",
        "if(model == \"stories15M\"):\n",
        "  download_url = \"https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.bin\"\n",
        "if(model == \"stories42M\"):\n",
        "  download_url = \"https://huggingface.co/karpathy/tinyllamas/resolve/main/stories42M.bin\"\n",
        "if(model == \"stories110M\"):\n",
        "  download_url = \"https://huggingface.co/karpathy/tinyllamas/resolve/main/stories110M.bin\"\n",
        "\n",
        "print(f\"download_url: {download_url}\")\n",
        "\n",
        "!wget $download_url\n",
        "\n",
        "model_file = model + \".bin\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OgAc3KjuT-NM"
      },
      "outputs": [],
      "source": [
        "#@title Generate Stories\n",
        "\n",
        "# Generate args\n",
        "max_token = 256 #@param {type:\"slider\", min:32, max:1024, step:32}\n",
        "temperature = 0.8 #@param {type:\"slider\", min:0.0, max:1, step:0.05}\n",
        "# top_p = 0.9 #@param {type:\"slider\", min:0.0, max:1.0, step:0.05}\n",
        "prompt = \"One day, Lily met a Shoggoth\" #@param {type:\"string\"}\n",
        "\n",
        "print(f\"model: {model_file}, max_token: {max_token}, temperature: {temperature}, top_p: {top_p}, prompt: {prompt}\")\n",
        "print(f\"----------------------------\\n\")\n",
        "\n",
        "cmd = f'python3 llama2.py {model_file} {temperature} {max_token} \"{prompt}\"'\n",
        "!{cmd}"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install huggingface_hub"
      ],
      "metadata": {
        "id": "3NDq_u_7YYjg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bH8Xo_HJWDsC"
      },
      "outputs": [],
      "source": [
        "#@title Run Meta's Llama 2 models\n",
        "\n",
        "#@markdown input your huggingface [access token](https://huggingface.co/settings/tokens) to download Meta's Llama 2 models.\n",
        "\n",
        "from huggingface_hub import snapshot_download\n",
        "\n",
        "token = \"replace to HF token\" #@param {type:\"string\"}\n",
        "path = snapshot_download(repo_id=\"meta-llama/Llama-2-7b\",cache_dir=\"Llama-2-7b\", use_auth_token=token)\n",
        "\n",
        "!python export_meta_llama_bin.py $path llama2_7b.bin\n",
        "\n",
        "print(\"python3 llama2.py llama2_7b.bin\\n\")\n",
        "!python3 llama2.py llama2_7b.bin"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "private_outputs": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}