Fix links broken in projects renaming

Author: strickvl

README.md

@@ -266,7 +266,7 @@ And finally, here are some other examples and use cases for inspiration:
 
 1. [E2E Batch Inference](examples/e2e/): Feature engineering, training, and inference pipelines for tabular machine learning.
 2. [Basic NLP with BERT](examples/e2e_nlp/): Feature engineering, training, and inference focused on NLP.
-3. [LLM RAG Pipeline with Langchain and OpenAI](https://github.com/zenml-io/zenml-projects/tree/main/llm-agents): Using Langchain to create a simple RAG pipeline.
+3. [LLM RAG Pipeline with Langchain and OpenAI](https://github.com/zenml-io/zenml-projects/tree/main/zenml-support-agent): Using Langchain to create a simple RAG pipeline.
 4. [Huggingface Model to Sagemaker Endpoint](https://github.com/zenml-io/zenml-projects/tree/main/huggingface-sagemaker): Automated MLOps on Amazon Sagemaker and HuggingFace
 5. [LLMops](https://github.com/zenml-io/zenml-projects/tree/main/llm-complete-guide): Complete guide to do LLM with ZenML
 
@@ -341,8 +341,8 @@ our GitHub repo.
 ## 📚 LLM-focused Learning Resources
 
 1. [LL Complete Guide - Full RAG Pipeline](https://github.com/zenml-io/zenml-projects/tree/main/llm-complete-guide) - Document ingestion, embedding management, and query serving
-2. [LLM Fine-Tuning Pipeline](https://github.com/zenml-io/zenml-projects/tree/main/llm-finetuning) - From data prep to deployed model
-3. [LLM Agents Example](https://github.com/zenml-io/zenml-projects/tree/main/llm-agents) - Track conversation quality and tool usage
+2. [LLM Fine-Tuning Pipeline](https://github.com/zenml-io/zenml-projects/tree/main/zencoder) - From data prep to deployed model
+3. [LLM Agents Example](https://github.com/zenml-io/zenml-projects/tree/main/zenml-support-agent) - Track conversation quality and tool usage
 
 ## 🤖 AI-Friendly Documentation with llms.txt
 

docs/book/how-to/pipeline-development/training-with-gpus/accelerate-distributed-training.md

@@ -49,7 +49,7 @@ The `run_with_accelerate` decorator accepts various arguments to configure your
 3. If `run_with_accelerate` is misused, it will raise a `RuntimeError` with a helpful message explaining the correct usage.
 
 {% hint style="info" %}
-To see a full example where Accelerate is used within a ZenML pipeline, check out our [llm-lora-finetuning](https://github.com/zenml-io/zenml-projects/blob/main/llm-lora-finetuning/README.md) project which leverages the distributed training functionalities while finetuning an LLM.
+To see a full example where Accelerate is used within a ZenML pipeline, check out our [llm-lora-finetuning](https://github.com/zenml-io/zenml-projects/blob/main/gamesense/README.md) project which leverages the distributed training functionalities while finetuning an LLM.
 {% endhint %}
 
 ## Ensure your container is Accelerate-ready

docs/book/user-guide/llmops-guide/finetuning-embeddings/evaluating-finetuned-embeddings.md

@@ -120,7 +120,7 @@ LLM-powered evaluations.
 The next section will cover [LLM finetuning and deployment](../finetuning-llms/) as the\
 final part of our LLMops guide. (This section is currently still a work in\
 progress, but if you're eager to try out LLM finetuning with ZenML, you can use[our LoRA\
-project](https://github.com/zenml-io/zenml-projects/blob/main/llm-lora-finetuning/README.md)\
+project](https://github.com/zenml-io/zenml-projects/blob/main/gamesense/README.md)\
 to get started. We also have [a\
 blogpost](https://www.zenml.io/blog/how-to-finetune-llama-3-1-with-zenml) guide which\
 takes you through[all the steps you need to finetune Llama 3.1](https://www.zenml.io/blog/how-to-finetune-llama-3-1-with-zenml) using GCP's Vertex AI with ZenML,\

docs/book/user-guide/llmops-guide/finetuning-llms/README.md

@@ -41,6 +41,6 @@ when you might need to finetune an LLM, how to evaluate the performance of what\
 you do as well as decisions around what data to use and so on.
 
 To follow along with the example explained in this guide, please follow the\
-instructions in [the `llm-lora-finetuning` repository](https://github.com/zenml-io/zenml-projects/tree/main/llm-lora-finetuning) where the full code is also\
+instructions in [the `llm-lora-finetuning` repository](https://github.com/zenml-io/zenml-projects/tree/main/gamesense) where the full code is also\
 available. This code can be run locally (if you have a GPU attached to your\
 machine) or using cloud compute as you prefer.

docs/book/user-guide/llmops-guide/finetuning-llms/evaluation-for-finetuning.md

@@ -94,7 +94,7 @@ When using Generalized evals, it's important to consider their limitations and c
 - [nervaluate](https://github.com/MantisAI/nervaluate) (for NER)
 
 It's easy to build in one of these frameworks into your ZenML pipeline. The
-implementation of evaluation in [the `llm-lora-finetuning` project](https://github.com/zenml-io/zenml-projects/tree/main/llm-lora-finetuning) is a good
+implementation of evaluation in [the `llm-lora-finetuning` project](https://github.com/zenml-io/zenml-projects/tree/main/gamesense) is a good
 example of how to do this. We used the `evaluate` library for ROUGE evaluation,
 but you could easily swap this out for another framework if you prefer. See [the previous section](finetuning-with-accelerate.md#implementation-details) for more details.
 

docs/book/user-guide/llmops-guide/finetuning-llms/finetuning-with-accelerate.md

@@ -19,7 +19,7 @@ structured meaning representations.
 For a
 full walkthrough of how to run the LLM finetuning yourself, visit [the LLM Lora
 Finetuning
-project](https://github.com/zenml-io/zenml-projects/tree/main/llm-lora-finetuning)
+project](https://github.com/zenml-io/zenml-projects/tree/main/gamesense)
 where you'll find instructions and the code.
 {% endhint %}