Upload InferenceTest.ipynb

InferenceTest.ipynb

@@ -10,28 +10,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "fa3d9de4-4e59-468f-92f0-b5f2ec55858d",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024-11-27 10:33:08.171106: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
-      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
-      "E0000 00:00:1732703588.241296    2164 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "E0000 00:00:1732703588.262674    2164 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "2024-11-27 10:33:08.443688: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
-      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from transformers import AutoProcessor, AutoTokenizer, AutoModelForCausalLM\n",
     "import torch\n",
     "import os\n",
     "\n",
+    "try:\n",
+    "    from google.colab import userdata\n",
+    "    HF_TOKEN = userdata.get('HF_TOKEN')\n",
+    "    os.environ['HF_TOKEN'] = HF_TOKEN\n",
+    "except:\n",
+    "    print(\"Not running in Google Colab, trying to get the HF_TOKEN from the environment\")\n",
+    "\n",
+    "\n",
     "if os.environ.get('HF_TOKEN') is None:\n",
     "    raise ValueError(\"You must set the HF_TOKEN environment variable to use this script, you also need to have access to the Llama 3.2 model family\")\n",
     "\n",