gh: update links in GitHub templates (#19592)

.github/ISSUE_TEMPLATE/2_refactor.yaml

@@ -34,7 +34,6 @@ body:
         - [**Metrics**](https://github.com/Lightning-AI/metrics):
           Machine learning metrics for distributed, scalable PyTorch applications.
           enables pure PyTorch users to scale their existing code on any kind of device while retaining full control over their own loops and optimization logic.
-        - [**Flash**](https://github.com/Lightning-AI/lightning-flash):
-          The fastest way to get a Lightning baseline! A collection of tasks for fast prototyping, baselining, fine-tuning, and solving problems with deep learning.
-        - [**Bolts**](https://github.com/Lightning-AI/lightning-bolts):
-          Pretrained SOTA Deep Learning models, callbacks, and more for research and production with PyTorch Lightning and PyTorch.
+        - [**GPT**](https://github.com/Lightning-AI/lit-GPT):
+          Hackable implementation of state-of-the-art open-source LLMs based on nanoGPT.
+          Supports flash attention, 4-bit and 8-bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed.

.github/ISSUE_TEMPLATE/3_feature_request.yaml

@@ -40,7 +40,6 @@ body:
         - [**Metrics**](https://github.com/Lightning-AI/metrics):
           Machine learning metrics for distributed, scalable PyTorch applications.
           enables pure PyTorch users to scale their existing code on any kind of device while retaining full control over their own loops and optimization logic.
-        - [**Flash**](https://github.com/Lightning-AI/lightning-flash):
-          The fastest way to get a Lightning baseline! A collection of tasks for fast prototyping, baselining, fine-tuning, and solving problems with deep learning.
-        - [**Bolts**](https://github.com/Lightning-AI/lightning-bolts):
-          Pretrained SOTA Deep Learning models, callbacks, and more for research and production with PyTorch Lightning and PyTorch.
+        - [**GPT**](https://github.com/Lightning-AI/lit-GPT):
+          Hackable implementation of state-of-the-art open-source LLMs based on nanoGPT.
+          Supports flash attention, 4-bit and 8-bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed.

.github/ISSUE_TEMPLATE/4_documentation.yaml

@@ -23,7 +23,6 @@ body:
         - [**Metrics**](https://github.com/Lightning-AI/metrics):
           Machine learning metrics for distributed, scalable PyTorch applications.
           enables pure PyTorch users to scale their existing code on any kind of device while retaining full control over their own loops and optimization logic.
-        - [**Flash**](https://github.com/Lightning-AI/lightning-flash):
-          The fastest way to get a Lightning baseline! A collection of tasks for fast prototyping, baselining, fine-tuning, and solving problems with deep learning.
-        - [**Bolts**](https://github.com/Lightning-AI/lightning-bolts):
-          Pretrained SOTA Deep Learning models, callbacks, and more for research and production with PyTorch Lightning and PyTorch.
+        - [**GPT**](https://github.com/Lightning-AI/lit-GPT):
+          Hackable implementation of state-of-the-art open-source LLMs based on nanoGPT.
+          Supports flash attention, 4-bit and 8-bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed.