gemma-2.ipynb

{
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   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-12-07T15:49:32.321239Z",
     "start_time": "2024-12-07T15:49:32.318747Z"
    }
   },
   "source": "from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig\n",
   "outputs": [],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-07T15:49:37.606204Z",
     "start_time": "2024-12-07T15:49:37.603566Z"
    }
   },
   "cell_type": "code",
   "source": "import torch\n",
   "id": "f6a3d5af026a01ca",
   "outputs": [],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-07T15:52:52.071476Z",
     "start_time": "2024-12-07T15:52:52.069526Z"
    }
   },
   "cell_type": "code",
   "source": "model_name_or_id = \"MehdiHosseiniMoghadam/AVA-Llama-3-V2\"\n",
   "id": "2d560d40641a5ccd",
   "outputs": [],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-07T15:52:52.965258Z",
     "start_time": "2024-12-07T15:52:52.582329Z"
    }
   },
   "cell_type": "code",
   "source": "model = AutoModelForCausalLM.from_pretrained(model_name_or_id, torch_dtype=torch.float16, device_map=\"auto\", low_cpu_mem_usage=True, load_in_8bit=True)",
   "id": "c0491fde9d8e5c7",
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "Using `low_cpu_mem_usage=True` or a `device_map` requires Accelerate: `pip install 'accelerate>=0.26.0'`",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mImportError\u001B[0m                               Traceback (most recent call last)",
      "Cell \u001B[0;32mIn[11], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m model \u001B[38;5;241m=\u001B[39m AutoModelForCausalLM\u001B[38;5;241m.\u001B[39mfrom_pretrained(model_name_or_id, torch_dtype\u001B[38;5;241m=\u001B[39mtorch\u001B[38;5;241m.\u001B[39mfloat16, device_map\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mauto\u001B[39m\u001B[38;5;124m\"\u001B[39m, low_cpu_mem_usage\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m, load_in_8bit\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n",
      "File \u001B[0;32m~/anaconda3/envs/Research/lib/python3.12/site-packages/transformers/models/auto/auto_factory.py:564\u001B[0m, in \u001B[0;36m_BaseAutoModelClass.from_pretrained\u001B[0;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001B[0m\n\u001B[1;32m    562\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;28mtype\u001B[39m(config) \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mcls\u001B[39m\u001B[38;5;241m.\u001B[39m_model_mapping\u001B[38;5;241m.\u001B[39mkeys():\n\u001B[1;32m    563\u001B[0m     model_class \u001B[38;5;241m=\u001B[39m _get_model_class(config, \u001B[38;5;28mcls\u001B[39m\u001B[38;5;241m.\u001B[39m_model_mapping)\n\u001B[0;32m--> 564\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m model_class\u001B[38;5;241m.\u001B[39mfrom_pretrained(\n\u001B[1;32m    565\u001B[0m         pretrained_model_name_or_path, \u001B[38;5;241m*\u001B[39mmodel_args, config\u001B[38;5;241m=\u001B[39mconfig, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mhub_kwargs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs\n\u001B[1;32m    566\u001B[0m     )\n\u001B[1;32m    567\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[1;32m    568\u001B[0m     \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mUnrecognized configuration class \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mconfig\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__class__\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m for this kind of AutoModel: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mcls\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__name__\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m.\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m    569\u001B[0m     \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mModel type should be one of \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m, \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;241m.\u001B[39mjoin(c\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__name__\u001B[39m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mfor\u001B[39;00m\u001B[38;5;250m \u001B[39mc\u001B[38;5;250m \u001B[39m\u001B[38;5;129;01min\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28mcls\u001B[39m\u001B[38;5;241m.\u001B[39m_model_mapping\u001B[38;5;241m.\u001B[39mkeys())\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m    570\u001B[0m )\n",
      "File \u001B[0;32m~/anaconda3/envs/Research/lib/python3.12/site-packages/transformers/modeling_utils.py:3372\u001B[0m, in \u001B[0;36mPreTrainedModel.from_pretrained\u001B[0;34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, *model_args, **kwargs)\u001B[0m\n\u001B[1;32m   3368\u001B[0m         \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[1;32m   3369\u001B[0m             \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mDeepSpeed Zero-3 is not compatible with `low_cpu_mem_usage=True` or with passing a `device_map`.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m   3370\u001B[0m         )\n\u001B[1;32m   3371\u001B[0m     \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m is_accelerate_available():\n\u001B[0;32m-> 3372\u001B[0m         \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mImportError\u001B[39;00m(\n\u001B[1;32m   3373\u001B[0m             \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mUsing `low_cpu_mem_usage=True` or a `device_map` requires Accelerate: `pip install \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124maccelerate>=\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mACCELERATE_MIN_VERSION\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m`\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m   3374\u001B[0m         )\n\u001B[1;32m   3376\u001B[0m \u001B[38;5;66;03m# handling bnb config from kwargs, remove after `load_in_{4/8}bit` deprecation.\u001B[39;00m\n\u001B[1;32m   3377\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m load_in_4bit \u001B[38;5;129;01mor\u001B[39;00m load_in_8bit:\n",
      "\u001B[0;31mImportError\u001B[0m: Using `low_cpu_mem_usage=True` or a `device_map` requires Accelerate: `pip install 'accelerate>=0.26.0'`"
     ]
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "85aedc7fbd4f6ff0"
  }
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