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ibm-granite · Oct 18, 2024 · 79566b3 · 79566b3
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diff --git a/notebooks/hfdemo/patchtsmixer_HF_blog.ipynb b/notebooks/hfdemo/patchtsmixer_HF_blog.ipynb
@@ -79,6 +79,10 @@
     "import os\n",
     "import random\n",
     "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import torch\n",
+    "\n",
     "# Third Party\n",
     "from transformers import (\n",
     "    EarlyStoppingCallback,\n",
@@ -87,9 +91,6 @@
     "    Trainer,\n",
     "    TrainingArguments,\n",
     ")\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import torch\n",
     "\n",
     "# First Party\n",
     "from tsfm_public.toolkit.dataset import ForecastDFDataset\n",
@@ -992,9 +993,7 @@
    ],
    "source": [
     "print(\"Loading pretrained model\")\n",
-    "finetune_forecast_model = PatchTSMixerForPrediction.from_pretrained(\n",
-    "    \"patchtsmixer/electricity/model/pretrain/\"\n",
-    ")\n",
+    "finetune_forecast_model = PatchTSMixerForPrediction.from_pretrained(\"patchtsmixer/electricity/model/pretrain/\")\n",
     "print(\"Done\")"
    ]
   },
@@ -1296,14 +1295,12 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {
     "vscode": {
      "languageId": "plaintext"
     }
    },
-   "outputs": [],
    "source": [
     "By doing a simple linear probing, MSE decreased from 0.3 to 0.271 achiving the SOTA results."
    ]
@@ -1511,9 +1508,7 @@
    ],
    "source": [
     "# Reload the model\n",
-    "finetune_forecast_model = PatchTSMixerForPrediction.from_pretrained(\n",
-    "    \"patchtsmixer/electricity/model/pretrain/\"\n",
-    ")\n",
+    "finetune_forecast_model = PatchTSMixerForPrediction.from_pretrained(\"patchtsmixer/electricity/model/pretrain/\")\n",
     "finetune_forecast_trainer = Trainer(\n",
     "    model=finetune_forecast_model,\n",
     "    args=finetune_forecast_args,\n",
@@ -1579,7 +1574,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.12.6"
   }
  },
  "nbformat": 4,

diff --git a/notebooks/hfdemo/ttm_getting_started.ipynb b/notebooks/hfdemo/ttm_getting_started.ipynb
@@ -101,6 +101,7 @@
    "source": [
     "import warnings\n",
     "\n",
+    "\n",
     "# Suppress all warnings\n",
     "warnings.filterwarnings(\"ignore\")"
    ]
@@ -128,7 +129,7 @@
     "# TTM Revision (1 or 2)\n",
     "TTM_REVISION = 2\n",
     "\n",
-    "# Context length, Or Length of the history. \n",
+    "# Context length, Or Length of the history.\n",
     "# Currently supported values are: 512/1024/1536 for TTM-R-2, and 512/1024 for TTM-R1\n",
     "CONTEXT_LENGTH = 512\n",
     "\n",
@@ -165,7 +166,7 @@
     "    if CONTEXT_LENGTH == 512:\n",
     "        TTM_MODEL_REVISION = \"main\"\n",
     "    elif CONTEXT_LENGTH == 1024:\n",
-    "        TTM_MODEL_REVISION=\"1024_96_v1\"\n",
+    "        TTM_MODEL_REVISION = \"1024_96_v1\"\n",
     "    else:\n",
     "        raise ValueError(f\"Unsupported CONTEXT_LENGTH for TTM_MODEL_PATH={TTM_MODEL_PATH}\")\n",
     "elif TTM_REVISION == 2:\n",
@@ -175,9 +176,9 @@
     "    if CONTEXT_LENGTH == 512:\n",
     "        TTM_MODEL_REVISION = \"main\"\n",
     "    elif CONTEXT_LENGTH == 1024:\n",
-    "        TTM_MODEL_REVISION=\"1024-96-r2\"\n",
+    "        TTM_MODEL_REVISION = \"1024-96-r2\"\n",
     "    elif CONTEXT_LENGTH == 1536:\n",
-    "        TTM_MODEL_REVISION=\"1536-96-r2\"\n",
+    "        TTM_MODEL_REVISION = \"1536-96-r2\"\n",
     "    else:\n",
     "        raise ValueError(f\"Unsupported CONTEXT_LENGTH for TTM_MODEL_PATH={TTM_MODEL_PATH}\")\n",
     "else:\n",
@@ -712,7 +713,9 @@
     }
    ],
    "source": [
-    "fewshot_finetune_eval(dataset_name=TARGET_DATASET, context_length=CONTEXT_LENGTH, batch_size=64, fewshot_percent=5, learning_rate=0.001)"
+    "fewshot_finetune_eval(\n",
+    "    dataset_name=TARGET_DATASET, context_length=CONTEXT_LENGTH, batch_size=64, fewshot_percent=5, learning_rate=0.001\n",
+    ")"
    ]
   },
   {
@@ -992,7 +995,12 @@
    ],
    "source": [
     "fewshot_finetune_eval(\n",
-    "    dataset_name=TARGET_DATASET, context_length=CONTEXT_LENGTH, batch_size=64, prediction_filter_length=48, fewshot_percent=5, learning_rate=None\n",
+    "    dataset_name=TARGET_DATASET,\n",
+    "    context_length=CONTEXT_LENGTH,\n",
+    "    batch_size=64,\n",
+    "    prediction_filter_length=48,\n",
+    "    fewshot_percent=5,\n",
+    "    learning_rate=None,\n",
     ")"
    ]
   },