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removed redundant model config definition from intro tutorials. Removed provider field in model config def in custom model config notebook
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+34
-72
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4 files changed

+34
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nemo/NeMo-Data-Designer/intro-tutorials/1-the-basics.ipynb

Lines changed: 11 additions & 18 deletions
Original file line numberDiff line numberDiff line change
@@ -99,14 +99,22 @@
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"- This list contains the models you can choose from (via the `model_alias` argument) during the generation process.\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Note**: \n",
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"The NeMo Data Designer Managed service has models available for you to use as well. You can use these models by referencing the appropriate model_alias for them.\n",
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"\n",
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"Please visit https://build.nvidia.com/nemo/data-designer to see the full list of models and their model aliases."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
108-
"# build.nvidia.com model endpoint\n",
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"model_id = \"nvidia/nvidia-nemotron-nano-9b-v2\"\n",
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"model_alias = \"nemotron-nano-v2\""
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]
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},
@@ -116,22 +124,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
119-
"config_builder = DataDesignerConfigBuilder(\n",
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" model_configs = [\n",
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" P.ModelConfig(\n",
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" alias=model_alias,\n",
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" provider=\"nvidiabuild\",\n",
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" model=model_id,\n",
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" inference_parameters=P.InferenceParameters(\n",
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" max_tokens=1024,\n",
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" temperature=0.6,\n",
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" top_p=0.95,\n",
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" timeout=120\n",
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" ),\n",
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" is_reasoner=True\n",
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" ),\n",
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" ]\n",
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")"
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"config_builder = DataDesignerConfigBuilder()"
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]
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},
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{

nemo/NeMo-Data-Designer/intro-tutorials/2-structured-outputs-and-jinja-expressions.ipynb

Lines changed: 11 additions & 18 deletions
Original file line numberDiff line numberDiff line change
@@ -102,14 +102,22 @@
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"- This list contains the models you can choose from (via the `model_alias` argument) during the generation process.\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
109+
"**Note**: \n",
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"The NeMo Data Designer Managed service has models available for you to use as well. You can use these models by referencing the appropriate model_alias for them.\n",
111+
"\n",
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"Please visit https://build.nvidia.com/nemo/data-designer to see the full list of models and their model aliases."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
111-
"# build.nvidia.com model endpoint\n",
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"model_id = \"nvidia/nvidia-nemotron-nano-9b-v2\"\n",
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"model_alias = \"nemotron-nano-v2\""
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]
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},
@@ -119,22 +127,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
122-
"config_builder = DataDesignerConfigBuilder(\n",
123-
" model_configs = [\n",
124-
" P.ModelConfig(\n",
125-
" alias=model_alias,\n",
126-
" provider=\"nvidiabuild\",\n",
127-
" model=model_id,\n",
128-
" inference_parameters=P.InferenceParameters(\n",
129-
" max_tokens=1024,\n",
130-
" temperature=0.6,\n",
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" top_p=0.95,\n",
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" timeout=120\n",
133-
" ),\n",
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" is_reasoner=True\n",
135-
" ),\n",
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" ]\n",
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")"
130+
"config_builder = DataDesignerConfigBuilder()"
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]
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},
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{

nemo/NeMo-Data-Designer/intro-tutorials/3-seeding-with-a-dataset.ipynb

Lines changed: 12 additions & 34 deletions
Original file line numberDiff line numberDiff line change
@@ -103,14 +103,22 @@
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"- This list contains the models you can choose from (via the `model_alias` argument) during the generation process.\n"
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]
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},
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{
107+
"cell_type": "markdown",
108+
"metadata": {},
109+
"source": [
110+
"**Note**: \n",
111+
"The NeMo Data Designer Managed service has models available for you to use as well. You can use these models by referencing the appropriate model_alias for them.\n",
112+
"\n",
113+
"Please visit https://build.nvidia.com/nemo/data-designer to see the full list of models and their model aliases."
114+
]
115+
},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
112-
"# build.nvidia.com model endpoint\n",
113-
"model_id = \"nvidia/nvidia-nemotron-nano-9b-v2\"\n",
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"model_alias = \"nemotron-nano-v2\""
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]
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},
@@ -120,22 +128,7 @@
120128
"metadata": {},
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"outputs": [],
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"source": [
123-
"config_builder = DataDesignerConfigBuilder(\n",
124-
" model_configs = [\n",
125-
" P.ModelConfig(\n",
126-
" alias=model_alias,\n",
127-
" provider=\"nvidiabuild\",\n",
128-
" model=model_id,\n",
129-
" inference_parameters=P.InferenceParameters(\n",
130-
" max_tokens=1024,\n",
131-
" temperature=0.6,\n",
132-
" top_p=0.95,\n",
133-
" timeout=120\n",
134-
" ),\n",
135-
" is_reasoner=True\n",
136-
" ),\n",
137-
" ]\n",
138-
")"
131+
"config_builder = DataDesignerConfigBuilder()"
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]
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},
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{
@@ -153,21 +146,6 @@
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"**Note**: At this time, we only support using a single file as the seed. If you have multiple files you would like to use as seeds, it is recommended you consolidated these into a single file. \n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
162-
"# if using the managed service of data designer, provide the api key here\n",
163-
"hf_token = getpass(\"Enter Huggingface Token here: \")\n",
164-
"\n",
165-
"if len(hf_token) > 0:\n",
166-
" print(\"✅ Huggingface Token received.\")\n",
167-
"else:\n",
168-
" print(\"❌ No Huggingface Token provided. Please enter your Huggingface Token.\")"
169-
]
170-
},
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{
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"cell_type": "code",
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"execution_count": null,
@@ -183,7 +161,7 @@
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" filename=\"train.jsonl\",\n",
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" sampling_strategy=\"shuffle\",\n",
185163
" with_replacement=False,\n",
186-
" datastore={\"endpoint\": \"https://huggingface.co\", \"token\": hf_token}\n",
164+
" datastore={\"endpoint\": \"https://huggingface.co\"}\n",
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")"
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]
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},

nemo/NeMo-Data-Designer/intro-tutorials/4-custom-model-configs.ipynb

Lines changed: 0 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -137,7 +137,6 @@
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" model_configs = [\n",
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" P.ModelConfig(\n",
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" alias=model_alias_static_temp,\n",
140-
" provider=\"nvidiabuild\",\n",
141140
" model=model_id,\n",
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" inference_parameters=P.InferenceParameters(\n",
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" max_tokens=1024,\n",
@@ -149,7 +148,6 @@
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" ),\n",
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" P.ModelConfig(\n",
151150
" alias=model_alias_variable_temp,\n",
152-
" provider=\"nvidiabuild\",\n",
153151
" model=model_id,\n",
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" inference_parameters=P.InferenceParameters(\n",
155153
" max_tokens=1024,\n",

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