fix small errors

getting-started/finetuning/finetune_llama4.md

@@ -1,6 +1,6 @@
 ## Fine-Tuning Tutorial for Llama4 Models with torchtune
 
-This tutorial shows how to perform Low-Rank Adaptation (LoRA) fine-tuning on Llama4 models using torchtune, based on the recent PR adding LoRA support for Llama4.
+This tutorial shows how to perform fine-tuning on Llama4 models using [torchtune](https://github.com/pytorch/torchtune?tab=readme-ov-file).
 
 ### Prerequisites
 
@@ -9,11 +9,11 @@ This tutorial shows how to perform Low-Rank Adaptation (LoRA) fine-tuning on Lla
 pip install --pre torchtune --extra-index-url https://download.pytorch.org/whl/nightly/cpu --no-cache-dir
 ```
 
-2. We also need Hugging Face access token (HF_TOKEN) for model download, please follow the instructions [here](https://huggingface.co/docs/hub/security-tokens) to get your own token.
+2. We also need Hugging Face access token (HF_TOKEN) for model download, please follow the instructions [here](https://huggingface.co/docs/hub/security-tokens) to get your own token. You will also need to gain model access to Llama4 models from [here](https://huggingface.co/collections/meta-llama/llama-4-67f0c30d9fe03840bc9d0164)
 
-### Tutorial
+### Steps
 1. Download Llama4 Weights
-Replace <HF_TOKEN> with your Hugging Face token:
+We will use `meta-llama/Llama-4-Scout-17B-16E-Instruct` as an example here. Replace <HF_TOKEN> with your Hugging Face token:
 
 ```bash
 tune download meta-llama/Llama-4-Scout-17B-16E-Instruct --output-dir /tmp/Llama-4-Scout-17B-16E-Instruct --hf-token $HF_TOKEN
@@ -27,23 +27,29 @@ tune download meta-llama/Llama-4-Scout-17B-16E-Instruct --output-dir /tmp/Llama-
 This retrieves the model weights, tokenizer from Hugging Face.
 
 2. Run LoRA Fine-Tuning for Llama4
+
 To run LoRA fine-tuning, use the following command:
 ```bash
 tune run --nproc_per_node 8 lora_finetune_distributed --config llama4/scout_17B_16E_lora
 ```
+This will run LoRA fine-tuning on Llama4 model with 8 GPUs. It will requires around 400GB gpu memory to do Llama4 Scout LoRA fine-tuning.
+
 You can add specific overrides through the command line. For example, to use a larger batch_size:
+
 ```bash
   tune run --nproc_per_node 8 lora_finetune_distributed --config llama4/scout_17B_16E_lora batch_size=4 dataset.packed=True tokenizer.max_seq_len=2048
 ```
-This will run LoRA fine-tuning on Llama4 model with 8 GPUs. It will requires around 400GB gpu memory to do Scout lora fine-tuning.
 
-The config llama4/scout_17B_16E_lora is a config file that specifies the model, tokenizer, and training parameters. The dataset.packed=True and tokenizer.max_seq_len=2048 are additional arguments that specify the dataset and tokenizer settings.To learn more about the available options, please refer to the [YAML config documentation](https://pytorch.org/torchtune/stable/deep_dives/configs.html#config-tutorial-label)
+The config llama4/scout_17B_16E_lora is a config file that specifies the model, tokenizer, and training parameters. The dataset.packed=True and tokenizer.max_seq_len=2048 are additional arguments that specify the dataset and tokenizer settings. To learn more about the available options, please refer to the [YAML config documentation](https://pytorch.org/torchtune/stable/deep_dives/configs.html#config-tutorial-label)
 
 With this setup, you can efficiently train LoRA adapters on Llama4 models using torchtune’s native recipes.
 
-3. Full Parameter Fine-Tuning for Llama4 (Optional)
+3. Full Parameter Fine-Tuning for Llama4
+
 To run full parameter fine-tuning, use the following command:
+
 ```bash
   tune run --nproc_per_node 4  --nproc_per_node 8 full_finetune_distributed --config llama4/scout_17B_16E_full batch_size=4 dataset.packed=True tokenizer.max_seq_len=2048
   ```
-This will run full parameter fine-tuning on Llama4 model with 8 GPUs. It will requires around 2200GB gpu memory to do Scout full parameter fine-tuning, which is about 4 8xH100 nodes.
+
+This will run full parameter fine-tuning on Llama4 model with 4 nodes. It will requires around 2200GB gpu memory to do Scout full parameter fine-tuning, which is about 4 8xH100 nodes.