fix: update customizer docs based on QA feedback

docs/customizer/models/embedding.mdx

@@ -42,7 +42,7 @@ Create a Model Entity for this embedding model:
 
 <Note>
 
-Embedding models only support **merged LoRA** (`peft` with `merge=True`). Unmerged LoRA adapters are not supported because the embedding NIM requires ONNX format, which cannot represent standalone adapters.
+Embedding models only support **merged LoRA** by setting `training.finetuning_type` to `"lora_merged"` in the Automodel job input. Unmerged LoRA adapters are not supported because the embedding NIM requires ONNX format, which cannot represent standalone adapters.
 
 </Note>
 ### Resource Requirements

docs/customizer/tutorials/embedding-customization-job.ipynb

@@ -449,7 +449,7 @@
         "\n",
         "#### Quick Setup Example\n",
         "\n",
-        "This tutorial fine-tunes [nvidia/llama-3.2-nv-embedqa-1b-v2](https://huggingface.co/nvidia/llama-3.2-nv-embedqa-1b-v2), NVIDIA embedding model optimized for question-answering and retrieval tasks."
+        "This tutorial fine-tunes [nvidia/llama-nemotron-embed-1b-v2](https://huggingface.co/nvidia/llama-nemotron-embed-1b-v2), an NVIDIA embedding model optimized for question-answering and retrieval tasks."
       ]
     },
     {
@@ -495,7 +495,7 @@
       "source": [
         "### 7. Create Base Model FileSet and Model Entity\n",
         "\n",
-        "Create a fileset pointing to the [nvidia/llama-3.2-nv-embedqa-1b-v2](https://huggingface.co/nvidia/llama-3.2-nv-embedqa-1b-v2) embedding model from HuggingFace, then create a Model Entity that references this fileset. Model downloading will take place at training time."
+        "Create a fileset pointing to the [nvidia/llama-nemotron-embed-1b-v2](https://huggingface.co/nvidia/llama-nemotron-embed-1b-v2) embedding model from HuggingFace, then create a Model Entity that references this fileset. Model downloading will take place at training time."
       ]
     },
     {
@@ -513,7 +513,7 @@
         "    base_model_fs = client.files.filesets.create(\n",
         "        workspace=\"default\",\n",
         "        name=MODEL_NAME,\n",
-        "        description=\"NVIDIA Llama 3.2 NV EmbedQA 1B v2 embedding model\",\n",
+        "        description=\"NVIDIA Llama Nemotron Embed 1B v2 embedding model\",\n",
         "        storage=HuggingfaceStorageConfigParam(\n",
         "            type=\"huggingface\",\n",
         "            # repo_id is the full model name from Hugging Face\n",
@@ -986,4 +986,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

docs/customizer/tutorials/embedding-customization-job.mdx

@@ -368,7 +368,7 @@ Get your credentials:
 
 #### Quick Setup Example
 
-This tutorial fine-tunes [nvidia/llama-3.2-nv-embedqa-1b-v2](https://huggingface.co/nvidia/llama-3.2-nv-embedqa-1b-v2), NVIDIA embedding model optimized for question-answering and retrieval tasks.
+This tutorial fine-tunes [nvidia/llama-nemotron-embed-1b-v2](https://huggingface.co/nvidia/llama-nemotron-embed-1b-v2), an NVIDIA embedding model optimized for question-answering and retrieval tasks.
 
 ```python
 # Create secrets for model access
@@ -404,7 +404,7 @@ print(f"NGC_API_KEY secret: {NGC_SECRET_NAME}")
 
 ### 7. Create Base Model FileSet and Model Entity
 
-Create a fileset pointing to the [nvidia/llama-3.2-nv-embedqa-1b-v2](https://huggingface.co/nvidia/llama-3.2-nv-embedqa-1b-v2) embedding model from HuggingFace, then create a Model Entity that references this fileset. Model downloading will take place at training time.
+Create a fileset pointing to the [nvidia/llama-nemotron-embed-1b-v2](https://huggingface.co/nvidia/llama-nemotron-embed-1b-v2) embedding model from HuggingFace, then create a Model Entity that references this fileset. Model downloading will take place at training time.
 
 ```python
 import time
@@ -418,7 +418,7 @@ try:
     base_model_fs = client.files.filesets.create(
         workspace="default",
         name=MODEL_NAME,
-        description="NVIDIA Llama 3.2 NV EmbedQA 1B v2 embedding model",
+        description="NVIDIA Llama Nemotron Embed 1B v2 embedding model",
         storage=HuggingfaceStorageConfigParam(
             type="huggingface",
             # repo_id is the full model name from Hugging Face