Merge pull request #96 from ibm-granite/naming

update links to reflect new naming
ibm-granite · Aug 1, 2024 · 27c684d · 27c684d
2 parents be059fd + 515cec9
commit 27c684d
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diff --git a/notebooks/hfdemo/patch_tsmixer_getting_started.ipynb b/notebooks/hfdemo/patch_tsmixer_getting_started.ipynb
@@ -301,7 +301,7 @@
    "source": [
     "## Testing with a `PatchTSMixer` model that was trained on the training part of the `ETTh1` data\n",
     "\n",
-    "A pre-trained model (on `ETTh1` data) is available at [ibm/patchtsmixer-etth1-forecasting](https://huggingface.co/ibm/patchtsmixer-etth1-forecasting)."
+    "A pre-trained model (on `ETTh1` data) is available at [ibm-granite/granite-timeseries-patchtsmixer](https://huggingface.co/ibm-granite/granite-timeseries-patchtsmixer)."
    ]
   },
   {
@@ -322,7 +322,7 @@
    "source": [
     "print(\"Loading pretrained model\")\n",
     "inference_forecast_model = PatchTSMixerForPrediction.from_pretrained(\n",
-    "    \"ibm/patchtsmixer-etth1-forecasting\"\n",
+    "    \"ibm-granite/granite-timeseries-patchtsmixer\"\n",
     ")\n",
     "print(\"Done\")"
    ]
@@ -885,7 +885,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,

diff --git a/notebooks/hfdemo/patch_tst_getting_started.ipynb b/notebooks/hfdemo/patch_tst_getting_started.ipynb
@@ -289,7 +289,7 @@
    "source": [
     "## Testing with a `PatchTST` model that was trained on the training part of the `ETTh1` data\n",
     "\n",
-    "A pre-trained model (on `ETTh1` data) is available at [ibm/patchtst-etth1-forecasting](https://huggingface.co/ibm/patchtst-etth1-forecasting)."
+    "A pre-trained model (on `ETTh1` data) is available at [ibm-granite/granite-timeseries-patchtst](https://huggingface.co/ibm-granite/granite-timeseries-patchtst)."
    ]
   },
   {
@@ -310,7 +310,7 @@
    "source": [
     "print(\"Loading pretrained model\")\n",
     "inference_forecast_model = PatchTSTForPrediction.from_pretrained(\n",
-    "    \"ibm/patchtst-etth1-forecasting\"\n",
+    "    \"ibm-granite/granite-timeseries-patchtst\"\n",
     ")\n",
     "print(\"Done\")"
    ]

diff --git a/notebooks/hfdemo/ttm_getting_started.ipynb b/notebooks/hfdemo/ttm_getting_started.ipynb
@@ -16,7 +16,7 @@
     "\n",
     "Note: Alternatively, this notebook can be modified to try the TTM-1024-96 model.\n",
     "\n",
-    "Pre-trained TTM models will be fetched from the [Hugging Face TTM Model Repository](https://huggingface.co/ibm/TTM)."
+    "Pre-trained TTM models will be fetched from the [Hugging Face TTM Model Repository](https://huggingface.co/ibm-granite/granite-timeseries-ttm-v1)."
    ]
   },
   {
@@ -141,12 +141,12 @@
     "    # Load model\n",
     "    if prediction_filter_length is None:\n",
     "        zeroshot_model = TinyTimeMixerForPrediction.from_pretrained(\n",
-    "            \"ibm/TTM\", revision=TTM_MODEL_REVISION\n",
+    "            \"ibm-granite/granite-timeseries-ttm-v1\", revision=TTM_MODEL_REVISION\n",
     "        )\n",
     "    else:\n",
     "        if prediction_filter_length <= forecast_length:\n",
     "            zeroshot_model = TinyTimeMixerForPrediction.from_pretrained(\n",
-    "                \"ibm/TTM\", revision=TTM_MODEL_REVISION, prediction_filter_length=prediction_filter_length\n",
+    "                \"ibm-granite/granite-timeseries-ttm-v1\", revision=TTM_MODEL_REVISION, prediction_filter_length=prediction_filter_length\n",
     "            )\n",
     "        else:\n",
     "            raise ValueError(f\"`prediction_filter_length` should be <= `forecast_length\")\n",
@@ -214,22 +214,22 @@
     "    if \"ett\" in dataset_name:\n",
     "        if prediction_filter_length is None:\n",
     "            finetune_forecast_model = TinyTimeMixerForPrediction.from_pretrained(\n",
-    "                \"ibm/TTM\", revision=TTM_MODEL_REVISION, head_dropout=0.7\n",
+    "                \"ibm-granite/granite-timeseries-ttm-v1\", revision=TTM_MODEL_REVISION, head_dropout=0.7\n",
     "            )\n",
     "        elif prediction_filter_length <= forecast_length:\n",
     "            finetune_forecast_model = TinyTimeMixerForPrediction.from_pretrained(\n",
-    "                \"ibm/TTM\", revision=TTM_MODEL_REVISION, head_dropout=0.7, prediction_filter_length=prediction_filter_length\n",
+    "                \"ibm-granite/granite-timeseries-ttm-v1\", revision=TTM_MODEL_REVISION, head_dropout=0.7, prediction_filter_length=prediction_filter_length\n",
     "            )\n",
     "        else:\n",
     "            raise ValueError(f\"`prediction_filter_length` should be <= `forecast_length\")\n",
     "    else:\n",
     "        if prediction_filter_length is None:\n",
     "            finetune_forecast_model = TinyTimeMixerForPrediction.from_pretrained(\n",
-    "                \"ibm/TTM\", revision=TTM_MODEL_REVISION,\n",
+    "                \"ibm-granite/granite-timeseries-ttm-v1\", revision=TTM_MODEL_REVISION,\n",
     "            )\n",
     "        elif prediction_filter_length <= forecast_length:\n",
     "            finetune_forecast_model = TinyTimeMixerForPrediction.from_pretrained(\n",
-    "                \"ibm/TTM\", revision=TTM_MODEL_REVISION, prediction_filter_length=prediction_filter_length\n",
+    "                \"ibm-granite/granite-timeseries-ttm-v1\", revision=TTM_MODEL_REVISION, prediction_filter_length=prediction_filter_length\n",
     "            )\n",
     "        else:\n",
     "            raise ValueError(f\"`prediction_filter_length` should be <= `forecast_length\")\n",

diff --git a/notebooks/tutorial/ttm_tutorial.ipynb b/notebooks/tutorial/ttm_tutorial.ipynb
@@ -16,7 +16,7 @@
     "\n",
     "Note: Alternatively, this notebook can be modified to try the TTM-1024-96 model.\n",
     "\n",
-    "Pre-trained TTM models will be fetched from the [Hugging Face TTM Model Repository](https://huggingface.co/ibm/TTM)."
+    "Pre-trained TTM models will be fetched from the [Hugging Face TTM Model Repository](https://huggingface.co/ibm-granite/granite-timeseries-ttm-v1)."
    ]
   },
   {
@@ -49,7 +49,7 @@
    ],
    "source": [
     "# Clone the ibm/tsfm\n",
-    "! git clone https://github.com/IBM/tsfm.git"
+    "! git clone https://github.com/IBM-granite/granite-tsfm.git"
    ]
   },
   {
@@ -1014,7 +1014,7 @@
     }
    ],
    "source": [
-    "zeroshot_model = TinyTimeMixerForPrediction.from_pretrained(\"ibm/TTM\", revision=TTM_MODEL_REVISION)\n",
+    "zeroshot_model = TinyTimeMixerForPrediction.from_pretrained(\"ibm-granite/granite-timeseries-ttm-v1\", revision=TTM_MODEL_REVISION)\n",
     "zeroshot_model"
    ]
   },
@@ -1300,7 +1300,7 @@
     }
    ],
    "source": [
-    "finetune_forecast_model = TinyTimeMixerForPrediction.from_pretrained(\"ibm/TTM\", revision=TTM_MODEL_REVISION, head_dropout=0.7)\n",
+    "finetune_forecast_model = TinyTimeMixerForPrediction.from_pretrained(\"ibm-granite/granite-timeseries-ttm-v1\", revision=TTM_MODEL_REVISION, head_dropout=0.7)\n",
     "finetune_forecast_model"
    ]
   },
@@ -1650,7 +1650,7 @@
     "### Q 19.\tZero-shot on channel 0 and 2 for etth1\n",
     "In your notebook, add `prediction_channel_indices=[0,2]` during model loading to forecast only 0th and 2nd channels. In this case, execute the following code and note the output shape.\n",
     "```\n",
-    "zeroshot_model = TinyTimeMixerForPrediction.from_pretrained(\"ibm/TTM\", revision=TTM_MODEL_REVISION, prediction_channel_indices=[0,2])\n",
+    "zeroshot_model = TinyTimeMixerForPrediction.from_pretrained(\"ibm-granite/granite-timeseries-ttm-v1\", revision=TTM_MODEL_REVISION, prediction_channel_indices=[0,2])\n",
     "output = zeroshot_model.forward(test_dataset[0]['past_values'].unsqueeze(0), return_loss=False)\n",
     "output.prediction_outputs.shape\n",
     "```"

diff --git a/notebooks/tutorial/ttm_tutorial_with_ans.ipynb b/notebooks/tutorial/ttm_tutorial_with_ans.ipynb
@@ -16,7 +16,7 @@
     "\n",
     "Note: Alternatively, this notebook can be modified to try the TTM-1024-96 model.\n",
     "\n",
-    "Pre-trained TTM models will be fetched from the [Hugging Face TTM Model Repository](https://huggingface.co/ibm/TTM)."
+    "Pre-trained TTM models will be fetched from the [Hugging Face TTM Model Repository](https://huggingface.co/ibm-granite/granite-timeseries-ttm-v1)."
    ]
   },
   {
@@ -43,7 +43,7 @@
    ],
    "source": [
     "# Clone the ibm/tsfm\n",
-    "! git clone https://github.com/IBM/tsfm.git"
+    "! git clone https://github.com/IBM-granite/granite-tsfm.git"
    ]
   },
   {
@@ -1009,7 +1009,7 @@
     }
    ],
    "source": [
-    "zeroshot_model = TinyTimeMixerForPrediction.from_pretrained(\"ibm/TTM\", revision=TTM_MODEL_REVISION)\n",
+    "zeroshot_model = TinyTimeMixerForPrediction.from_pretrained(\"ibm-granite/granite-timeseries-ttm-v1\", revision=TTM_MODEL_REVISION)\n",
     "zeroshot_model"
    ]
   },
@@ -1295,7 +1295,7 @@
     }
    ],
    "source": [
-    "finetune_forecast_model = TinyTimeMixerForPrediction.from_pretrained(\"ibm/TTM\", revision=TTM_MODEL_REVISION, head_dropout=0.7)\n",
+    "finetune_forecast_model = TinyTimeMixerForPrediction.from_pretrained(\"ibm-granite/granite-timeseries-ttm-v1\", revision=TTM_MODEL_REVISION, head_dropout=0.7)\n",
     "finetune_forecast_model"
    ]
   },
@@ -1694,7 +1694,7 @@
     }
    ],
    "source": [
-    "zeroshot_model = TinyTimeMixerForPrediction.from_pretrained(\"ibm/TTM\", revision=TTM_MODEL_REVISION)\n",
+    "zeroshot_model = TinyTimeMixerForPrediction.from_pretrained(\"ibm-granite/granite-timeseries-ttm-v1\", revision=TTM_MODEL_REVISION)\n",
     "temp_dir = tempfile.mkdtemp()\n",
     "# zeroshot_trainer\n",
     "zeroshot_trainer = Trainer(\n",
@@ -1755,7 +1755,7 @@
     }
    ],
    "source": [
-    "zeroshot_model = TinyTimeMixerForPrediction.from_pretrained(\"ibm/TTM\", revision=TTM_MODEL_REVISION, prediction_filter_length=24)\n",
+    "zeroshot_model = TinyTimeMixerForPrediction.from_pretrained(\"ibm-granite/granite-timeseries-ttm-v1\", revision=TTM_MODEL_REVISION, prediction_filter_length=24)\n",
     "temp_dir = tempfile.mkdtemp()\n",
     "# zeroshot_trainer\n",
     "zeroshot_trainer = Trainer(\n",
@@ -1959,7 +1959,7 @@
     }
    ],
    "source": [
-    "finetune_forecast_model = TinyTimeMixerForPrediction.from_pretrained(\"ibm/TTM\", revision=TTM_MODEL_REVISION, head_dropout=0.7)\n",
+    "finetune_forecast_model = TinyTimeMixerForPrediction.from_pretrained(\"ibm-granite/granite-timeseries-ttm-v1\", revision=TTM_MODEL_REVISION, head_dropout=0.7)\n",
     "finetune_forecast_model"
    ]
   },
@@ -2201,7 +2201,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "finetune_forecast_model = TinyTimeMixerForPrediction.from_pretrained(\"ibm/TTM\", revision=TTM_MODEL_REVISION, head_dropout=0.7, loss=\"mae\")"
+    "finetune_forecast_model = TinyTimeMixerForPrediction.from_pretrained(\"ibm-granite/granite-timeseries-ttm-v1\", revision=TTM_MODEL_REVISION, head_dropout=0.7, loss=\"mae\")"
    ]
   },
   {
@@ -2425,7 +2425,7 @@
     "### Q 19.\tZero-shot on channel 0 and 2 for etth1\n",
     "In your notebook, add `prediction_channel_indices=[0,2]` during model loading to forecast only 0th and 2nd channels. In this case, execute the following code and note the output shape.\n",
     "```\n",
-    "zeroshot_model = TinyTimeMixerForPrediction.from_pretrained(\"ibm/TTM\", revision=TTM_MODEL_REVISION, prediction_channel_indices=[0,2])\n",
+    "zeroshot_model = TinyTimeMixerForPrediction.from_pretrained(\"ibm-granite/granite-timeseries-ttm-v1\", revision=TTM_MODEL_REVISION, prediction_channel_indices=[0,2])\n",
     "output = zeroshot_model.forward(test_dataset[0]['past_values'].unsqueeze(0), return_loss=False)\n",
     "output.prediction_outputs.shape\n",
     "```"
@@ -2449,7 +2449,7 @@
     }
    ],
    "source": [
-    "zeroshot_model = TinyTimeMixerForPrediction.from_pretrained(\"ibm/TTM\", revision=TTM_MODEL_REVISION, prediction_channel_indices=[0,2])\n",
+    "zeroshot_model = TinyTimeMixerForPrediction.from_pretrained(\"ibm-granite/granite-timeseries-ttm-v1\", revision=TTM_MODEL_REVISION, prediction_channel_indices=[0,2])\n",
     "output = zeroshot_model.forward(test_dataset[0]['past_values'].unsqueeze(0), return_loss=False)\n",
     "output.prediction_outputs.shape"
    ]