docs: fix 11 dead links in the documentation (#12320)

- compatibility table: voxtral.c, OuteTTS and VoxCPM repos live under
  antirez, edwko and OpenBMB
- distributed inferencing: llama.cpp RPC README moved to tools/rpc under ggml-org
- customize-model: the phi-2 example config moved to LocalAI-examples, and
  embedded/models was replaced by the gallery
- model-gallery: malformed URL; link the gallery index
- GPU acceleration: ROCm install guide moved
- integrations: Wave Terminal docs page moved to ai-presets

Assisted-by: Claude:claude-fable-5-1

Signed-off-by: Pratik Gandhi <travpreneur@gmail.com>

docs/content/features/GPU-acceleration.md

@@ -243,7 +243,7 @@ The devices in the following list have been tested with `hipblas` images.
 
 1. Check your GPU LLVM target is compatible with the version of ROCm. This can be found in the [LLVM Docs](https://llvm.org/docs/AMDGPUUsage.html).
 2. Check which ROCm version is compatible with your LLVM target and your chosen OS (pay special attention to supported kernel versions). See the [ROCm compatibility matrix](https://rocm.docs.amd.com/en/latest/compatibility/compatibility-matrix.html).
-3. Install your chosen version of the `dkms` and `rocm` (it is recommended that the native package manager be used for this process for any OS as version changes are executed more easily via this method if updates are required). Take care to restart after installing `amdgpu-dkms` and before installing `rocm`, for details regarding this see the [ROCm installation documentation](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/how-to/native-install/index.html).
+3. Install your chosen version of the `dkms` and `rocm` (it is recommended that the native package manager be used for this process for any OS as version changes are executed more easily via this method if updates are required). Take care to restart after installing `amdgpu-dkms` and before installing `rocm`, for details regarding this see the [ROCm installation documentation](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/install-methods/package-manager-index.html).
 4. Deploy. Yes it's that easy.
 
 #### Setup Example (Docker/containerd)

docs/content/features/distributed_inferencing.md

@@ -98,7 +98,7 @@ LLAMACPP_GRPC_SERVERS="address1:port,address2:port" local-ai run
 ```
 The workload on the LocalAI server will then be distributed across the specified nodes.
 
-Alternatively, you can build the RPC workers/server following the llama.cpp [README](https://github.com/ggerganov/llama.cpp/blob/master/examples/rpc/README.md), which is compatible with LocalAI.
+Alternatively, you can build the RPC workers/server following the llama.cpp [README](https://github.com/ggml-org/llama.cpp/blob/master/tools/rpc/README.md), which is compatible with LocalAI.
 
 ## Manual example (worker)
 

docs/content/features/model-gallery.md

@@ -373,7 +373,7 @@ curl $LOCALAI/models/apply -H "Content-Type: application/json" -d '{
 where:
 - `localai` is the repository. It is optional and can be omitted. If the repository is omitted LocalAI will search the model by name in all the repositories. In the case the same model name is present in both galleries the first match wins.
 - `bert-embeddings` is the model name in the gallery
-  (read its [config here](https://github.com/mudler/LocalAI/tree/master/gallery/blob/main/bert-embeddings.yaml)).
+  (read its [config here](https://github.com/mudler/LocalAI/blob/master/gallery/index.yaml)).
 
 ### Model variants
 

docs/content/getting-started/customize-model.md

@@ -25,17 +25,17 @@ Here's an example to initiate the **phi-2** model:
 docker run -p 8080:8080 localai/localai:{{< version >}} https://gist.githubusercontent.com/mudler/ad601a0488b497b69ec549150d9edd18/raw/a8a8869ef1bb7e3830bf5c0bae29a0cce991ff8d/phi-2.yaml
 ```
 
-You can also check all the embedded models configurations [here](https://github.com/mudler/LocalAI/tree/master/embedded/models).
+You can also check all the embedded models configurations [here](https://github.com/mudler/LocalAI/tree/master/gallery).
 
 {{% notice tip %}}
-The model configurations used in the quickstart are accessible here: [https://github.com/mudler/LocalAI/tree/master/embedded/models](https://github.com/mudler/LocalAI/tree/master/embedded/models). Contributions are welcome; please feel free to submit a Pull Request.
+The model configurations used in the quickstart are accessible here: [https://github.com/mudler/LocalAI/tree/master/gallery](https://github.com/mudler/LocalAI/tree/master/gallery). Contributions are welcome; please feel free to submit a Pull Request.
 
-The `phi-2` model configuration from the quickstart is expanded from [https://github.com/mudler/LocalAI/blob/master/examples/configurations/phi-2.yaml](https://github.com/mudler/LocalAI/blob/master/examples/configurations/phi-2.yaml).
+The `phi-2` model configuration from the quickstart is expanded from [https://github.com/mudler/LocalAI-examples/blob/main/configurations/phi-2.yaml](https://github.com/mudler/LocalAI-examples/blob/main/configurations/phi-2.yaml).
  {{% /notice %}}
 
 ## Example: Customizing the Prompt Template
 
-To modify the prompt template, create a Github gist or a Pastebin file, and copy the content from [https://github.com/mudler/LocalAI/blob/master/examples/configurations/phi-2.yaml](https://github.com/mudler/LocalAI/blob/master/examples/configurations/phi-2.yaml). Alter the fields as needed:
+To modify the prompt template, create a Github gist or a Pastebin file, and copy the content from [https://github.com/mudler/LocalAI-examples/blob/main/configurations/phi-2.yaml](https://github.com/mudler/LocalAI-examples/blob/main/configurations/phi-2.yaml). Alter the fields as needed:
 
 ```yaml
 name: phi-2

docs/content/integrations.md

@@ -98,7 +98,7 @@ availability may lag upstream releases.
 - [AnythingLLM](https://github.com/Mintplex-Labs/anything-llm)
 - [Logseq GPT3 OpenAI plugin](https://github.com/briansunter/logseq-plugin-gpt3-openai)
 - [CodeGPT (JetBrains)](https://plugins.jetbrains.com/plugin/21056-codegpt) - Custom OpenAI-compatible endpoints
-- [Wave Terminal](https://docs.waveterm.dev/features/supportedLLMs/localai) - Native LocalAI support
+- [Wave Terminal](https://docs.waveterm.dev/ai-presets) - Native LocalAI support
 - [Obsidian BMO Chatbot](https://github.com/longy2k/obsidian-bmo-chatbot)
 - [spark](https://github.com/cedriking/spark)
 - [openops (Mattermost)](https://github.com/mattermost/openops)

docs/content/reference/compatibility-table.md

@@ -45,7 +45,7 @@ All backends listed here can be installed on demand from the [Backend Gallery]({
 | [moonshine](https://github.com/moonshine-ai/moonshine) | Ultra-fast transcription for low-end devices (ONNX) | CPU, CUDA 12/13, Metal |
 | [parakeet.cpp](https://github.com/mudler/parakeet.cpp) | C++/GGML port of NVIDIA NeMo Parakeet (tdt/ctc/rnnt/hybrid), with cache-aware streaming | CPU, CUDA 12/13, ROCm, Intel SYCL, Vulkan, Metal, Jetson L4T |
 | [CrispASR](https://github.com/CrispStrobe/CrispASR) | Unified speech engine (whisper.cpp fork) supporting Parakeet, Canary, and many ASR architectures, plus TTS | CPU, CUDA 12/13, ROCm, Intel SYCL, Vulkan, Metal, Jetson L4T |
-| [voxtral](https://github.com/mudler/voxtral.c) | Voxtral Realtime 4B speech-to-text in pure C | CPU, Metal |
+| [voxtral](https://github.com/antirez/voxtral.c) | Voxtral Realtime 4B speech-to-text in pure C | CPU, Metal |
 | [Qwen3-ASR](https://github.com/QwenLM/Qwen3-ASR) | Qwen3 automatic speech recognition | CPU, CUDA 12/13, ROCm, Intel SYCL, Metal, Jetson L4T |
 | [NeMo](https://github.com/NVIDIA/NeMo) | NVIDIA NeMo ASR toolkit | CPU, CUDA 12/13, ROCm, Intel SYCL, Metal |
 | [sherpa-onnx](https://k2-fsa.github.io/sherpa/onnx/) | Sherpa-ONNX ASR (Whisper, Paraformer, SenseVoice) and TTS | CPU, CUDA 12, Metal |
@@ -70,10 +70,10 @@ All backends listed here can be installed on demand from the [Backend Gallery]({
 | [OmniVoice](https://github.com/ServeurpersoCom/omnivoice.cpp) | Native C++/GGML TTS with voice cloning, voice design, and streaming | CPU, CUDA 12/13, ROCm, Intel SYCL, Vulkan, Metal, Jetson L4T |
 | [fish-speech](https://github.com/fishaudio/fish-speech) | High-quality TTS with voice cloning | CPU, CUDA 12/13, ROCm, Intel SYCL, Metal, Jetson L4T |
 | [Pocket TTS](https://github.com/kyutai-labs/pocket-tts) | Lightweight CPU-efficient TTS with voice cloning | CPU, CUDA 12/13, ROCm, Intel SYCL, Metal, Jetson L4T |
-| [OuteTTS](https://github.com/OuteAI/outetts) | TTS with custom speaker voices | CPU, CUDA 12 |
+| [OuteTTS](https://github.com/edwko/OuteTTS) | TTS with custom speaker voices | CPU, CUDA 12 |
 | [faster-qwen3-tts](https://github.com/andimarafioti/faster-qwen3-tts) | Real-time Qwen3-TTS with CUDA graph capture | CPU, CUDA 12/13, Jetson L4T |
 | [NeuTTS Air](https://github.com/neuphonic/neutts-air) | Instant voice cloning, on-device TTS | CPU, CUDA 12, ROCm |
-| [VoxCPM](https://github.com/ModelBest/VoxCPM) | Expressive end-to-end TTS | CPU, CUDA 12/13, ROCm, Intel SYCL, Metal |
+| [VoxCPM](https://github.com/OpenBMB/VoxCPM) | Expressive end-to-end TTS | CPU, CUDA 12/13, ROCm, Intel SYCL, Metal |
 | [Kitten TTS](https://github.com/KittenML/KittenTTS) | Kitten TTS model | CPU, Metal |
 | [Supertonic](https://github.com/supertone-inc/supertonic) | Lightning-fast on-device multilingual TTS via ONNX | CPU |
 | [MLX-Audio](https://github.com/Blaizzy/mlx-audio) | Audio models on Apple Silicon | CPU, CUDA 12/13, Metal, Jetson L4T |