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DarwinAnim8or
/
gpt-grug-125m

Text Generation
Transformers
PyTorch
Safetensors
English
gpt_neo
grug
caveman
fun
Inference Endpoints
Model card Files Community
1
gpt-grug-125m
File size: 2,948 Bytes 
b539400
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u7RAqjzj4ylm"
      },
      "outputs": [],
      "source": [
        "!pip install happytransformer"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from happytransformer import HappyGeneration\n",
        "\n",
        "happy_gen = HappyGeneration(\"GPT-NEO\", \"EleutherAI/gpt-neo-125M\")"
      ],
      "metadata": {
        "id": "4V9hd8bQ41HD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://huggingface.co./datasets/DarwinAnim8or/grug/resolve/main/grug-training.txt"
      ],
      "metadata": {
        "id": "hz3fzo5W9ppf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from happytransformer import GENTrainArgs \n",
        "\n",
        "args = GENTrainArgs(learning_rate =1e-5, num_train_epochs = 2)\n",
        "happy_gen.train(\"grug-training.txt\", args=args)"
      ],
      "metadata": {
        "id": "Yl4wNVvK5Bex"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from happytransformer import GENSettings\n",
        "args_top_k = GENSettings(no_repeat_ngram_size=3, do_sample=True,top_k=50, temperature=0.7, max_length=50, early_stopping=False)"
      ],
      "metadata": {
        "id": "LXi7hXFtBLpN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result = happy_gen.generate_text(\"\"\"Person: \"Hello grug\"\n",
        "Grug: \"hello person\"\n",
        "###\n",
        "Person: \"how are you grug\"\n",
        "Grug: \"grug doing ok. grug find many berry. good for tribe.\"\n",
        "###\n",
        "Person: \"what does grug think of new spear weapon?\"\n",
        "Grug: \"grug no like new spear weapon. grug stick bigger. spear too small, break easy\"\n",
        "###\n",
        "Person: \"what does grug think of football?\"\n",
        "Grug: \\\"\"\"\", args=args_top_k)\n",
        "#print(result)\n",
        "print(result.text)"
      ],
      "metadata": {
        "id": "ih4KihPy_U_h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#To save the model, run this cell.\n",
        "happy_gen.save(\"gpt-grug-125m-epoch4/\")"
      ],
      "metadata": {
        "id": "LFUPtXAo_dTz"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}