minor fixes

Signed-off-by: HuiyingLi <[email protected]>
HuiyingLi · Oct 14, 2024 · 189f91a · 189f91a
1 parent 23b2017
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diff --git a/tutorials/llm/nemo2-peft.ipynb b/tutorials/llm/nemo2-peft.ipynb
@@ -8,15 +8,15 @@
     "\n",
     "## Parameter-Efficient Fine-Tuning (PEFT)\n",
     "\n",
-    "This notebook aims to demonstrate how to adapt or customize foundation models to improve performance on specific tasks using nemo2.\n",
+    "This notebook aims to demonstrate how to adapt or customize foundation models to improve performance on specific tasks using NeMo 2.0.\n",
     "\n",
     "This optimization process is known as fine-tuning, which involves adjusting the weights of a pre-trained foundation model with custom data.\n",
     "\n",
-    "Considering that foundation models can be significantly large, a variant of fine-tuning has gained traction recently known as PEFT. PEFT encompasses several methods, including P-Tuning, LoRA, Adapters, IA3, etc. NeMo2.0 currently supports Low-Rank Adaptation(LoRA) method.\n",
+    "Considering that foundation models can be significantly large, a variant of fine-tuning has gained traction recently known as PEFT. PEFT encompasses several methods, including P-Tuning, LoRA, Adapters, IA3, etc. NeMo 2.0 currently supports Low-Rank Adaptation(LoRA) method.\n",
     "\n",
-    "This playbook involves applying LoRA to the Llama3 using nemo2. \n",
+    "This playbook involves applying LoRA to the Llama3 using NeMo 2.0. \n",
     "\n",
-    "## NeMo2.0\n",
+    "## NeMo 2.0\n",
     "\n",
     "In NeMo 1.0, the main interface for configuring experiments is through YAML files. This approach allows for a declarative way to set up experiments, but it has limitations in terms of flexibility and programmatic control. NeMo 2.0 is an update on the NeMo Framework which introduces several significant improvements over its predecessor, NeMo 1.0, enhancing flexibility, performance, and scalability.\n",
     "\n",
@@ -429,7 +429,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Optional: Launch with [NeMoRun](https://github.com/NVIDIA/NeMo-Run)\n",
+    "## Optional: Launch with [NeMo-Run](https://github.com/NVIDIA/NeMo-Run)\n",
     "Alternatively, we could use launch PEFT jobs using existing [recipes](https://github.com/NVIDIA/NeMo/tree/main/nemo/collections/llm/recipes) from NeMo-Run. A recipe in NeMo is a python file that defines a complete configuration for training or fine-tuning an LLM. Each recipe typically includes:\n",
     "1. Model configuration: Defines the architecture and hyperparameters of the LLM.\n",
     "2. Training configuration: Specifies settings for the PyTorch Lightning Trainer, including distributed training strategies.\n",

diff --git a/tutorials/llm/nemo2-sft.ipynb b/tutorials/llm/nemo2-sft.ipynb
@@ -10,7 +10,7 @@
     "\n",
     "Often we want to adapt or customize foundation models to be more performant on our specific task. Fine-tuning refers to how we can modify the weights of a pre-trained foundation model with additional custom data. Supervised fine-tuning (SFT) refers to unfreezing all the weights and layers in our model and training on a newly labeled set of examples. We can fine-tune to incorporate new, domain-specific knowledge, or teach the foundation model what type of response to provide. One specific type of SFT is also referred to as “instruction tuning” where we use SFT to teach a model to follow instructions better. In this playbook will demostrate how to perform SFT with Llama3-8b using NeMo 2.0.\n",
     "\n",
-    "## NeMo2.0\n",
+    "## NeMo 2.0\n",
     "\n",
     "In NeMo 1.0, the main interface for configuring experiments is through YAML files. This approach allows for a declarative way to set up experiments, but it has limitations in terms of flexibility and programmatic control. NeMo 2.0 is an update on the NeMo Framework which introduces several significant improvements over its predecessor, NeMo 1.0, enhancing flexibility, performance, and scalability.\n",
     "\n",
@@ -552,7 +552,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Optional: Launch with [NeMoRun](https://github.com/NVIDIA/NeMo-Run)\n",
+    "## Optional: Launch with [NeMo-Run](https://github.com/NVIDIA/NeMo-Run)\n",
     "Alternatively, we could use launch SFT jobs using existing [recipes](https://github.com/NVIDIA/NeMo/tree/main/nemo/collections/llm/recipes) from NeMo-Run. A recipe in NeMo is a python file that defines a complete configuration for training or fine-tuning an LLM. Each recipe typically includes:\n",
     "1. Model configuration: Defines the architecture and hyperparameters of the LLM.\n",
     "2. Training configuration: Specifies settings for the PyTorch Lightning Trainer, including distributed training strategies.\n",