remove_layer_mentions

Nixtla · Sep 24, 2024 · 7bc1b5d · 7bc1b5d
1 parent a7b6e73
commit 7bc1b5d
Show file tree

Hide file tree

Showing 4 changed files with 6 additions and 6 deletions.
diff --git a/nbs/docs/capabilities/forecast/07_finetuning.ipynb b/nbs/docs/capabilities/forecast/07_finetuning.ipynb
@@ -146,7 +146,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "By default, only the last layer of the model is finetuned. We can finetune more layers using `finetune_depth` parameter. "
+    "By default, only a small amount of finetuning is applied (`finetune_depth=1`). We can increase the intensity of finetuning by increasing the `finetune_depth` parameter. Note that increasing `finetune_depth` and `finetune_steps` increases wall time for generating predictions."
    ]
   },
   {

diff --git a/nbs/docs/tutorials/06_finetuning.ipynb b/nbs/docs/tutorials/06_finetuning.ipynb
@@ -326,7 +326,7 @@
     "### 3.1 Control the level of fine-tuning with `finetune_depth`\n",
     "It is also possible to control the depth of fine-tuning with the `finetune_depth` parameter.\n",
     "\n",
-    "`finetune_depth` takes values among `[1, 2, 3, 4, 5]`. By default, it is set to 1, which means that fine-tuning is shallow, whereas a value of 5 means the maximum amount of fine-tuning is done. Increasing `finetune_depth` increases the time to generate predictions."
+    "`finetune_depth` takes values among `[1, 2, 3, 4, 5]`. By default, it is set to 1, which means a small amount of fine-tuning is applied, whereas a value of 5 means the maximum amount of fine-tuning is done. Increasing `finetune_depth` increases the time to generate predictions."
    ]
   },
   {

diff --git a/nbs/src/nixtla_client.ipynb b/nbs/src/nixtla_client.ipynb
@@ -1392,8 +1392,8 @@
     "            Number of steps used to finetune TimeGPT in the\n",
     "            new data.\n",
     "        finetune_depth: int (default=1)\n",
-    "            Depth of finetuning, on a scale from 1 to 5. 1 means only the last layer is finetuned\n",
-    "            and 5 means the whole model if finetuned.\n",
+    "            The depth of the finetuning. Uses a scale from 1 to 5, where 1 means little finetuning,\n",
+    "            and 5 means that the entire model is finetuned. By default, the value is set to 1.\n",
     "        finetune_loss : str (default='default')\n",
     "            Loss function to use for finetuning. Options are: `default`, `mae`, `mse`, `rmse`, `mape`, and `smape`.\n",
     "        clean_ex_first : bool (default=True)\n",

diff --git a/nixtla/nixtla_client.py b/nixtla/nixtla_client.py
@@ -1344,8 +1344,8 @@ def cross_validation(
             Number of steps used to finetune TimeGPT in the
             new data.
         finetune_depth: int (default=1)
-            Depth of finetuning, on a scale from 1 to 5. 1 means only the last layer is finetuned
-            and 5 means the whole model if finetuned.
+            The depth of the finetuning. Uses a scale from 1 to 5, where 1 means little finetuning,
+            and 5 means that the entire model is finetuned. By default, the value is set to 1.
         finetune_loss : str (default='default')
             Loss function to use for finetuning. Options are: `default`, `mae`, `mse`, `rmse`, `mape`, and `smape`.
         clean_ex_first : bool (default=True)