{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aba711ad-331a-4c27-b732-be5886f70599",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip install -q -U transformers peft accelerate optimum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2fa956a0-882e-4df7-8af4-30a91efb1fb2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: auto-gptq in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (0.4.2+cu117)\n",
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      "Requirement already satisfied: datasets in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (from auto-gptq) (2.14.4)\n",
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      "Requirement already satisfied: nvidia-cufft-cu11==10.9.0.58 in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (from torch>=1.13.0->auto-gptq) (10.9.0.58)\n",
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      "Requirement already satisfied: nvidia-curand-cu11==10.2.10.91 in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (from torch>=1.13.0->auto-gptq) (10.2.10.91)\n",
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      "Requirement already satisfied: idna<4,>=2.5 in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (from requests->transformers>=4.31.0->auto-gptq) (3.4)\n",
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      "Requirement already satisfied: MarkupSafe>=2.0 in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (from jinja2->torch>=1.13.0->auto-gptq) (2.1.3)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (from pandas->datasets->auto-gptq) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /home/studio-lab-user/.conda/envs/default/lib/python3.9/site-packages (from pandas->datasets->auto-gptq) (2023.3)\n",
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     ]
    }
   ],
   "source": [
    "!pip install auto-gptq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "89705dea-d988-4ece-8073-54970626f4f7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9540122492f14f46ba75ebce474c28b7",
       "version_major": 2,
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "model_id": "2e25472cc600484ca211d91ca59f0d36",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
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    },
    {
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    },
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       "model_id": "f21bb47869cb4841b65ca603c7449453",
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     },
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    },
    {
     "data": {
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       "model_id": "7154fe572cf949e38cbeaa1e212281f7",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "426dd05bb0a743c79b9604564809a5a1",
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      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "66fab430ded94aada339c52da8cabaf7",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "3b65f4ac6a764c919996c5dd750feaa0",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "222828ac2eb44eefa1de3ad41cb81cf4",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "850fb340b7104c3a8b7d8fedda825690",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
    "\n",
    "model_id = \"agonh/MythoMax22b-Falseblock-GPT\"\n",
    "model = AutoModelForCausalLM.from_pretrained(model_id, device_map=\"auto\")\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d295d782-866a-4c60-9d03-18777a777568",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "text = \"Tell me about AI\"\n",
    "inputs = tokenizer(text, return_tensors=\"pt\").to(0)\n",
    "\n",
    "out = model.generate(**inputs, max_new_tokens=500)\n",
    "print(tokenizer.decode(out[0], skip_special_tokens=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea9720ad-d3ca-4a5f-8cf6-1846ea3e1e13",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
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