Adding reference in other readmes

getting-started/README.md

@@ -21,5 +21,6 @@ If you are new to developing with Meta Llama models, this is where you should st
 * The [Build_with_Llama API](./build_with_llama_api.ipynb) notebook highlights some of the features of [Llama API](https://llama.developer.meta.com?utm_source=llama-cookbook&utm_medium=readme&utm_campaign=getting_started).
 * The [inference](./inference/) folder contains scripts to deploy Llama for inference on server and mobile. See also [3p_integrations/vllm](../3p-integrations/vllm/) and [3p_integrations/tgi](../3p-integrations/tgi/) for hosting Llama on open-source model servers.
 * The [RAG](./RAG/) folder contains a simple Retrieval-Augmented Generation application using Llama.
-* The [finetuning](./finetuning/) folder contains resources to help you finetune Llama on your custom datasets, for both single- and multi-GPU setups. The scripts use the native llama-cookbook finetuning code found in [finetuning.py](../src/llama_cookbook/finetuning.py) which supports these features.
+* The [finetuning](./finetuning/) folder contains resources to help you finetune Llama on your custom datasets, for both single- and multi-GPU setups. The scripts use the native llama-cookbook finetuning code found in [finetuning.py](../src/llama_cookbook/finetuning.py) which supports these features:
+  * **NEW:** [Vision fine-tuning recipe](./finetuning/vision/README.md) for Llama 3.2 11B Vision - Learn how to fine-tune multimodal models for document understanding with 98% accuracy on structured data extraction!
 * The [llama-tools](./llama-tools/) folder contains resources to help you use Llama tools, such as [llama-prompt-ops](../llama-tools/llama-prompt-ops_101.ipynb).

getting-started/finetuning/README.md

@@ -13,6 +13,9 @@ If you are new to fine-tuning techniques, check out [an overview](./LLM_finetuni
 > [!TIP]
 > If you want to try finetuning Meta Llama 3 in a Jupyter notebook you can find a quickstart notebook [here](./quickstart_peft_finetuning.ipynb)
 
+> [!NOTE]
+> **New: Vision Fine-tuning Recipe** - Looking to fine-tune Llama 3.2 11B Vision for structured data extraction? Check out our comprehensive [vision fine-tuning recipe](./vision/README.md) that achieves 98% accuracy on document understanding tasks using torchtune, with detailed benchmarking comparing LoRA vs Full Parameter Fine-Tuning approaches.
+
 
 ## How to configure finetuning settings?