added smaples correctly

docs/source/index.mdx

@@ -23,95 +23,4 @@ Hugging Face DLCs are open source and licensed under Apache 2.0 within the [Goog
 You have two options to take advantage of these DLCs as a Google Cloud customer:
 
 1. To [get started](https://huggingface.co/blog/google-cloud-model-garden), you can use our no-code integrations within Vertex AI or GKE.
-2. For more advanced scenarios, you can pull the containers from the Google Cloud Artifact Registry directly in your environment. [Here](https://github.com/huggingface/Google-Cloud-Containers/tree/main/examples) is a list of notebooks examples.
-
-## Features & benefits 🔥
-
-The Hugging Face DLCs provide ready-to-use, tested environments to train and deploy Hugging Face models. They can be used in combination with Google Cloud offerings including Google Kubernetes Engine (GKE) and Vertex AI. GKE is a fully-managed Kubernetes service in Google Cloud that can be used to deploy and operate containerized applications at scale using Google Cloud's infrastructure. Vertex AI is a Machine Learning (ML) platform that lets you train and deploy ML models and AI applications, and customize Large Language Models (LLMs).
-
-### One command is all you need
-
-With the new Hugging Face DLCs, train cutting-edge Transformers-based NLP models in a single line of code. The Hugging Face PyTorch DLCs for training come with all the libraries installed to run a single command e.g. via TRL CLI to fine-tune LLMs on any setting, either single-GPU, single-node multi-GPU, and more.
-
-### Accelerate machine learning from science to production
-
-In addition to Hugging Face DLCs, we created a first-class Hugging Face library for inference, [`huggingface-inference-toolkit`](https://github.com/huggingface/huggingface-inference-toolkit), that comes with the Hugging Face PyTorch DLCs for inference, with full support on serving any PyTorch model on Google Cloud.
-
-Deploy your trained models for inference with just one more line of code or select [any of the 170,000+ publicly available models from the model Hub](https://huggingface.co/models?library=pytorch,transformers&sort=trending) and deploy them on either Vertex AI or GKE.
-
-### High-performance text generation and embedding
-
-Besides the PyTorch-oriented DLCs, Hugging Face also provides high-performance inference for both text generation and embedding models via the Hugging Face DLCs for both [Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) and [Text Embeddings Inference (TEI)](https://github.com/huggingface/text-embeddings-inference), respectively.
-
-The Hugging Face DLC for TGI enables you to deploy [any of the +140,000 text generation inference supported models from the Hugging Face Hub](https://huggingface.co/models?other=text-generation-inference&sort=trending), or any custom model as long as [its architecture is supported within TGI](https://huggingface.co/docs/text-generation-inference/supported_models).
-
-The Hugging Face DLC for TEI enables you to deploy [any of the +10,000 embedding, re-ranking or sequence classification supported models from the Hugging Face Hub](https://huggingface.co/models?other=text-embeddings-inference&sort=trending), or any custom model as long as [its architecture is supported within TEI](https://huggingface.co/docs/text-embeddings-inference/en/supported_models).
-
-Additionally, these DLCs come with full support for Google Cloud meaning that deploying models from Google Cloud Storage (GCS) is also straight forward and requires no configuration.
-
-### Built-in performance
-
-Hugging Face DLCs feature built-in performance optimizations for PyTorch to train models faster. The DLCs also give you the flexibility to choose a training infrastructure that best aligns with the price/performance ratio for your workload.
-
-The Hugging Face Training DLCs are fully integrated with Google Cloud, enabling the use of [the latest generation of instances available on Google Cloud Compute Engine](https://cloud.google.com/products/compute?hl=en).
-
-Hugging Face Inference DLCs provide you with production-ready endpoints that scale quickly with your Google Cloud environment, built-in monitoring, and a ton of enterprise features.
-
----
-
-Read more about both Vertex AI in [their official documentation](https://cloud.google.com/vertex-ai/docs) and GKE in [their official documentation](https://cloud.google.com/kubernetes-engine/docs).
-
-## Resources, Documentation & Examples 📄
-
-Learn how to use Hugging Face in Google Cloud by reading our blog posts, documentation and examples below.
-
-### Blog posts
-
-- [Hugging Face and Google partner for open AI collaboration](https://huggingface.co/blog/gcp-partnership)
-- [Google Cloud TPUs made available to Hugging Face users](https://huggingface.co/blog/tpu-inference-endpoints-spaces)
-- [Making thousands of open LLMs bloom in the Vertex AI Model Garden](https://huggingface.co/blog/google-cloud-model-garden)
-- [Deploy Meta Llama 3.1 405B on Google Cloud Vertex AI](https://huggingface.co/blog/llama31-on-vertex-ai)
-
-### Documentation
-
-- [Google Cloud Hugging Face Deep Learning Containers](https://cloud.google.com/deep-learning-containers/docs/choosing-container#hugging-face)
-- [Google Cloud public Artifact Registry for DLCs](https://console.cloud.google.com/artifacts/docker/deeplearning-platform-release/us/gcr.io)
-- [Serve Gemma open models using GPUs on GKE with Hugging Face TGI](https://cloud.google.com/kubernetes-engine/docs/tutorials/serve-gemma-gpu-tgi)
-- [Generative AI on Vertex - Use Hugging Face text generation models](https://cloud.google.com/vertex-ai/generative-ai/docs/open-models/use-hugging-face-models)
-
-### Examples
-
-- [All examples](https://github.com/huggingface/Google-Cloud-Containers/tree/main/examples)
-
-#### GKE
-
-- Training
-
-  - [Full SFT fine-tuning of Gemma 2B in a multi-GPU instance with TRL on GKE](https://github.com/huggingface/Google-Cloud-Containers/blob/main/examples/gke/trl-full-fine-tuning)
-  - [LoRA SFT fine-tuning of Mistral 7B v0.3 in a single GPU instance with TRL on GKE](https://github.com/huggingface/Google-Cloud-Containers/blob/main/examples/gke/trl-lora-fine-tuning)
-
-- Inference
-
-  - [Deploying Llama3 8B with Text Generation Inference (TGI) on GKE](https://github.com/huggingface/Google-Cloud-Containers/tree/main/examples/gke/tgi-deployment)
-  - [Deploying Qwen2 7B Instruct with Text Generation Inference (TGI) from a GCS Bucket on GKE](https://github.com/huggingface/Google-Cloud-Containers/tree/main/examples/gke/tgi-from-gcs-deployment)
-  - [Deploying Snowflake's Arctic Embed (M) with Text Embeddings Inference (TEI) on GKE](https://github.com/huggingface/Google-Cloud-Containers/tree/main/examples/gke/tei-deployment)
-  - [Deploying BGE Base v1.5 (English) with Text Embeddings Inference (TEI) from a GCS Bucket on GKE](https://github.com/huggingface/Google-Cloud-Containers/tree/main/examples/gke/tei-from-gcs-deployment)
-
-#### Vertex AI
-
-- Training
-
-  - [Full SFT fine-tuning of Mistral 7B v0.3 in a multi-GPU instance with TRL on Vertex AI](https://github.com/huggingface/Google-Cloud-Containers/blob/main/examples/vertex-ai/notebooks/trl-full-sft-fine-tuning-on-vertex-ai)
-  - [LoRA SFT fine-tuning of Mistral 7B v0.3 in a single GPU instance with TRL on Vertex AI](https://github.com/huggingface/Google-Cloud-Containers/blob/main/examples/vertex-ai/notebooks/trl-lora-sft-fine-tuning-on-vertex-ai)
-
-- Inference
-
-  - [Deploying a BERT model for a text classification task using huggingface-inference-toolkit for a Custom Prediction Routine (CPR) on Vertex AI](https://github.com/huggingface/Google-Cloud-Containers/tree/main/examples/vertex-ai/notebooks/deploy-bert-on-vertex-ai)
-  - [Deploying an embedding model with Text Embeddings Inference (TEI) on Vertex AI](https://github.com/huggingface/Google-Cloud-Containers/tree/main/examples/vertex-ai/notebooks/deploy-embedding-on-vertex-ai)
-  - [Deploying Gemma 7B Instruct with Text Generation Inference (TGI) on Vertex AI](https://github.com/huggingface/Google-Cloud-Containers/tree/main/examples/vertex-ai/notebooks/deploy-gemma-on-vertex-ai)
-  - [Deploying Gemma 7B Instruct with Text Generation Inference (TGI) from a GCS Bucket on Vertex AI](https://github.com/huggingface/Google-Cloud-Containers/tree/main/examples/vertex-ai/notebooks/deploy-gemma-from-gcs-on-vertex-ai)
-  - [Deploying FLUX with Hugging Face PyTorch DLCs for Inference on Vertex AI](https://github.com/huggingface/Google-Cloud-Containers/tree/main/examples/vertex-ai/notebooks/deploy-flux-on-vertex-ai)
-
-- Evaluation
-
-  - [Evaluating open LLMs with Vertex AI and Gemini](https://github.com/huggingface/Google-Cloud-Containers/tree/main/examples/vertex-ai/notebooks/evaluate-llms-with-vertex-ai)
+2. For more advanced scenarios, you can pull the containers from the Google Cloud Artifact Registry directly in your environment. [Here](https://github.com/huggingface/Google-Cloud-Containers/tree/main/examples) is a list of notebooks examples.

docs/source/resources.mdx

@@ -50,8 +50,14 @@ Learn how to use Hugging Face in Google Cloud by reading our blog posts, Google
   - [Deploy FLUX with PyTorch Inference DLC on Vertex AI](https://github.com/huggingface/Google-Cloud-Containers/tree/main/examples/vertex-ai/notebooks/deploy-flux-on-vertex-ai/vertex-notebook.ipynb)
   - [Deploy Meta Llama 3.1 405B with TGI DLC on Vertex AI](https://github.com/huggingface/Google-Cloud-Containers/tree/main/examples/vertex-ai/notebooks/deploy-llama-3-1-405b-on-vertex-ai/vertex-notebook.ipynb)
 
+
+- Evaluation
+
+  - [Evaluating open LLMs with Vertex AI and Gemini](https://github.com/huggingface/Google-Cloud-Containers/tree/main/examples/vertex-ai/notebooks/evaluate-llms-with-vertex-ai)
+
+
 ### (Preview) Cloud Run
 
 - Inference
 
-    - [Deploy Meta Llama 3.1 with TGI DLC on Cloud Run](https://github.com/huggingface/Google-Cloud-Containers/tree/main/examples/cloud-run/tgi-deployment)
+    - [Deploy Meta Llama 3.1 with TGI DLC on Cloud Run](https://github.com/huggingface/Google-Cloud-Containers/tree/main/examples/cloud-run/tgi-deployment)