From 28b00ec771e4dff79e203ab01b0cc8ee64fa1e5d Mon Sep 17 00:00:00 2001
From: Yingge He <yinggeh@nvidia.com>
Date: Tue, 3 Feb 2026 09:03:04 -0800
Subject: [PATCH] Fix broken links

---
 README.md                    | 88 +++++++++++++++++-------------------
 docs/build.md                |  5 +-
 docs/encoder_decoder.md      |  6 +--
 docs/guided_decoding.md      |  2 +-
 docs/llama_multi_instance.md |  6 +--
 docs/lora.md                 |  2 +-
 docs/model_config.md         |  8 ++--
 docs/multimodal.md           |  2 +-
 docs/whisper.md              |  2 +-
 9 files changed, 57 insertions(+), 64 deletions(-)

diff --git a/README.md b/README.md
index 9c64ed57..18c3d653 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,5 @@
 <!--
-# Copyright 2024-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# Copyright 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 #
 # Redistribution and use in source and binary forms, with or without
 # modification, are permitted provided that the following conditions
@@ -30,7 +30,7 @@
 The Triton backend for [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM).
 You can learn more about Triton backends in the [backend repo](https://github.com/triton-inference-server/backend).
 The goal of TensorRT-LLM Backend is to let you serve [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM)
-models with Triton Inference Server. The [inflight_batcher_llm](./inflight_batcher_llm/)
+models with Triton Inference Server. The [inflight_batcher_llm](https://github.com/triton-inference-server/TensorRT-LLM/tree/main/triton_backend/all_models/inflight_batcher_llm)
 directory contains the C++ implementation of the backend supporting inflight
 batching, paged attention and more.
 
@@ -154,7 +154,7 @@ trtllm-build --checkpoint_dir ./c-model/gpt2/fp16/4-gpu \
         --output_dir /engines/gpt/fp16/4-gpu
 ```
 
-See [here](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/gpt) for
+See [here](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/models/core/gpt) for
 more details on the parameters.
 
 #### Prepare the Model Repository
@@ -162,22 +162,22 @@ more details on the parameters.
 Next, create the
 [model repository](https://github.com/triton-inference-server/server/blob/main/docs/user_guide/model_repository.md)
 that will be used by the Triton server. The models can be found in the
-[all_models](./tensorrt_llm/triton_backend/all_models) folder. The folder contains six groups of models:
-- [`disaggregated_serving`](./tensorrt_llm/triton_backend/all_models/disaggregated_serving): Using the C++ TensorRT-LLM backend to run disaggregated serving.
-- [`gpt`](./tensorrt_llm/triton_backend/all_models/gpt): Using TensorRT-LLM pure Python runtime. This model is deprecated and will be removed in a future release.
-- [`inflight_batcher_llm`](./tensorrt_llm/triton_backend/all_models/inflight_batcher_llm/)`: Using the C++
+[all_models](https://github.com/NVIDIA/TensorRT-LLM/tree/main/triton_backend/all_models) folder. The folder contains six groups of models:
+- [`disaggregated_serving`](https://github.com/NVIDIA/TensorRT-LLM/tree/main/triton_backend/all_models/disaggregated_serving): Using the C++ TensorRT-LLM backend to run disaggregated serving.
+- [`gpt`](https://github.com/NVIDIA/TensorRT-LLM/tree/main/triton_backend/all_models/gpt): Using TensorRT-LLM pure Python runtime. This model is deprecated and will be removed in a future release.
+- [`inflight_batcher_llm`](https://github.com/NVIDIA/TensorRT-LLM/tree/main/triton_backend/all_models/inflight_batcher_llm)`: Using the C++
 TensorRT-LLM backend with the executor API, which includes the latest features
 including inflight batching.
 
 There are five models in
-[all_models/inflight_batcher_llm](./tensorrt_llm/triton_backend/all_models/inflight_batcher_llm) that will
+[all_models/inflight_batcher_llm](https://github.com/NVIDIA/TensorRT-LLM/tree/main/triton_backend/all_models/inflight_batcher_llm) that will
 be used in this example:
 
 | Model | Description |
 | :------------: | :---------------: |
 | `ensemble` | This model is used to chain the preprocessing, tensorrt_llm and postprocessing models together. |
 | `preprocessing` | This model is used for tokenizing, meaning the conversion from prompts(string) to input_ids(list of ints). |
-| `tensorrt_llm` | This model is a wrapper of your TensorRT-LLM model and is used for inferencing. Input specification can be found [here](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/advanced/inference-request.md) |
+| `tensorrt_llm` | This model is a wrapper of your TensorRT-LLM model and is used for inferencing. Input specification can be found [here](https://github.com/triton-inference-server/tensorrtllm_backend/blob/main/docs/model_config.md#model-input-and-output) |
 | `postprocessing` | This model is used for de-tokenizing, meaning the conversion from output_ids(list of ints) to outputs(string). |
 | `tensorrt_llm_bls` | This model can also be used to chain the preprocessing, tensorrt_llm and postprocessing models together. |
 
@@ -194,14 +194,14 @@ please see the [model config](./docs/model_config.md#tensorrt_llm_bls-model) sec
 mkdir /triton_model_repo
 cp -r /app/all_models/inflight_batcher_llm/* /triton_model_repo/
 ```
-- [`llmapi`](./tensorrt_llm/triton_backend/all_models/llmapi/): Using TensorRT-LLM LLM API with pytorch backend.
-- [`multimodal`](./tensorrt_llm/triton_backend/all_models/multimodal/): Using TensorRT-LLM python runtime for multimodal models. See [`multimodal.md`](./docs/multimodal.md) for more details.
-- [`whisper`](./tensorrt_llm/triton_backend/all_models/whisper/): Using TensorRT-LLM python runtime for Whisper. See [`whisper.md`](./docs/whisper.md) for more details.
+- [`llmapi`](https://github.com/triton-inference-server/TensorRT-LLM/tree/main/triton_backend/all_models/llmapi): Using TensorRT-LLM LLM API with pytorch backend.
+- [`multimodal`](https://github.com/triton-inference-server/TensorRT-LLM/tree/main/triton_backend/all_models/multimodal): Using TensorRT-LLM python runtime for multimodal models. See [`multimodal.md`](./docs/multimodal.md) for more details.
+- [`whisper`](https://github.com/triton-inference-server/TensorRT-LLM/tree/main/triton_backend/all_models/whisper): Using TensorRT-LLM python runtime for Whisper. See [`whisper.md`](./docs/whisper.md) for more details.
 
 #### Modify the Model Configuration
 Use the script to fill in the parameters in the model configuration files. For
 optimal performance or custom parameters, please refer to
-[perf_best_practices](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/performance/perf-best-practices.md).
+[performance-tuning-guide](https://github.com/NVIDIA/TensorRT-LLM/tree/main/docs/source/legacy/performance/performance-tuning-guide).
 For more details on the model configuration and the parameters that can be
 modified, please refer to the [model config](./docs/model_config.md) section.
 
@@ -301,11 +301,11 @@ Which should return a result similar to (formatted for readability):
 ##### Using the client scripts
 
 You can refer to the client scripts in the
-[inflight_batcher_llm/client](./tensorrt_llm/triton_backend/inflight_batcher_llm/client) to see how to send
+[inflight_batcher_llm/client](https://github.com/NVIDIA/TensorRT-LLM/tree/main/triton_backend/inflight_batcher_llm/client) to see how to send
 requests via Python scripts.
 
 Below is an example of using
-[inflight_batcher_llm_client](./tensorrt_llm/triton_backend/inflight_batcher_llm/client/inflight_batcher_llm_client.py)
+[inflight_batcher_llm_client](https://github.com/NVIDIA/TensorRT-LLM/tree/main/triton_backend/inflight_batcher_llm/client/inflight_batcher_llm_client.py)
 to send requests to the `tensorrt_llm` model.
 
 ```bash
@@ -361,14 +361,14 @@ engine (or `--gather_all_token_logits` to enable both at the same time). For
 more setting details about these two flags, please refer to
 [build.py](https://github.com/NVIDIA/TensorRT-LLM/blob/main/tensorrt_llm/commands/build.py)
 or
-[gpt_runtime](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/advanced/gpt-runtime.md).
+[gpt_runtime](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/legacy/advanced/gpt-runtime.md).
 
 After launching the server, you could get the output of logits by passing the
 corresponding parameters `--return-context-logits` and/or
 `--return-generation-logits` in the client scripts
-([end_to_end_grpc_client.py](./tensorrt_llm/triton_backend/inflight_batcher_llm/client/end_to_end_grpc_client.py)
+([end_to_end_grpc_client.py](https://github.com/NVIDIA/TensorRT-LLM/tree/main/triton_backend/inflight_batcher_llm/client/end_to_end_grpc_client.py)
 and
-[inflight_batcher_llm_client.py](./tensorrt_llm/triton_backend/inflight_batcher_llm/client/inflight_batcher_llm_client.py)).
+[inflight_batcher_llm_client.py](https://github.com/NVIDIA/TensorRT-LLM/tree/main/triton_backend/inflight_batcher_llm/client/inflight_batcher_llm_client.py)).
 
 For example:
 
@@ -423,7 +423,7 @@ with a given batch index. An output tensor named `batch_index` is associated
 with each response to indicate which batch index this response corresponds to.
 
 The client script
-[end_to_end_grpc_client.py](./tensorrt_llm/triton_backend/inflight_batcher_llm/client/end_to_end_grpc_client.py)
+[end_to_end_grpc_client.py](https://github.com/NVIDIA/TensorRT-LLM/blob/main/triton_backend/inflight_batcher_llm/client/end_to_end_grpc_client.py)
 demonstrates how a client can send requests with batch size > 1 and consume the
 responses returned from Triton. When passing `--batch-inputs` to the client
 script, the client will create a request with multiple prompts, and use the
@@ -448,16 +448,16 @@ to the [support matrix](https://nvidia.github.io/TensorRT-LLM/reference/support-
 
 - LLaMa
   - [End to end workflow to run llama 7b with Triton](https://github.com/triton-inference-server/tensorrtllm_backend/blob/main/docs/llama.md)
-  - [Build and run a LLaMA model in TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/llama)
+  - [Build and run a LLaMA model in TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/models/core/llama)
   - [Llama Multi-instance](https://github.com/triton-inference-server/tensorrtllm_backend/blob/main/docs/llama_multi_instance.md)
-  - [Deploying Hugging Face Llama2-7b Model in Triton](https://github.com/triton-inference-server/tutorials/blob/main/Popular_Models_Guide/Llama2/trtllm_guide.md#infer-with-tensorrt-llm-backend)
+  - [Deploying Hugging Face Llama2-7b Model in Triton](https://github.com/triton-inference-server/tutorials/blob/main/Popular_Models_Guide/Llama2/trtllm_guide.md)
 
 - Gemma
   - [End to end workflow to run sp model with Triton](https://github.com/triton-inference-server/tensorrtllm_backend/blob/main/docs/gemma.md)
-  - [Run Gemma on TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/gemma)
+  - [Run Gemma on TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/models/core/gemma)
 
 - Mistral
-  - [Build and run a Mixtral model in TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM/blob/main/examples/mixtral/README.md)
+  - [Build and run a Mixtral model in TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM/blob/main/examples/models/core/mixtral/README.md)
 
 - Multi-modal
   - [End to end workflow to run multimodal models(e.g. BLIP2-OPT, LLava1.5-7B, VILA) with Triton](https://github.com/triton-inference-server/tensorrtllm_backend/blob/main/docs/multimodal.md)
@@ -575,7 +575,7 @@ trtllm-build --checkpoint_dir ./tllm_checkpoint_mixtral_8gpu \
 ```
 
 See the
-[doc](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/advanced/expert-parallelism.md)
+[doc](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/legacy/advanced/expert-parallelism.md)
 to learn more about how TensorRT-LLM expert parallelism works in Mixture of Experts (MoE).
 
 ### MIG Support
@@ -588,21 +588,19 @@ for more details on how to run TRT-LLM models and Triton with MIG.
 
 The scheduler policy helps the batch manager adjust how requests are scheduled
 for execution. There are two scheduler policies supported in TensorRT-LLM,
-`MAX_UTILIZATION` and `GUARANTEED_NO_EVICT`. See the
-[batch manager design](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/advanced/batch-manager.md#gptmanager-design)
-to learn more about how scheduler policies work. You can specify the scheduler
+`MAX_UTILIZATION` and `GUARANTEED_NO_EVICT`. You can specify the scheduler
 policy via the `batch_scheduler_policy` parameter in the
-[model config](./docs/model_config.md#tensorrt_llm_model) of tensorrt_llm model.
+[model config](./docs/model_config.md#tensorrt_llm-model) of tensorrt_llm model.
 
 ### Key-Value Cache
 
 See the
-[KV Cache](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/advanced/gpt-attention.md#kv-cache)
+[KV Cache](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/legacy/advanced/gpt-attention.md#kv-cache)
 section for more details on how TensorRT-LLM supports KV cache. Also, check out
-the [KV Cache Reuse](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/kv_cache_reuse.md)
+the [KV Cache Reuse](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/legacy/advanced/kv-cache-reuse.md)
 documentation to learn more about how to enable KV cache reuse when building the
 TRT-LLM engine. Parameters for KV cache can be found in the
-[model config](./docs/model_config.md#tensorrt_llm_model) of tensorrt_llm model.
+[model config](./docs/model_config.md#tensorrt_llm-model) of tensorrt_llm model.
 
 ### Decoding
 
@@ -610,30 +608,30 @@ TRT-LLM engine. Parameters for KV cache can be found in the
 
 TensorRT-LLM supports various decoding modes, including top-k, top-p,
 top-k top-p, beam search Medusa, ReDrafter, Lookahead and Eagle. See the
-[Sampling Parameters](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/advanced/gpt-runtime.md#sampling-parameters)
+[Sampling Parameters](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/legacy/advanced/gpt-runtime.md#sampling-parameters)
 section to learn more about top-k, top-p, top-k top-p and beam search decoding.
 Please refer to the
-[speculative decoding documentation](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/advanced/speculative-decoding.md)
+[speculative decoding documentation](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/legacy/advanced/speculative-decoding.md)
 for more details on Medusa, ReDrafter, Lookahead and Eagle.
 
 Parameters for decoding modes can be found in the
-[model config](./docs/model_config.md#tensorrt_llm_model) of tensorrt_llm model.
+[model config](./docs/model_config.md#tensorrt_llm-model) of tensorrt_llm model.
 
 #### Speculative Decoding
 
 See the
-[Speculative Decoding](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/speculative_decoding.md)
+[Speculative Decoding](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/legacy/advanced/speculative-decoding.md)
 documentation to learn more about how TensorRT-LLM supports speculative decoding
 to improve the performance. The parameters for speculative decoding can be found
-in the [model config](./docs/model_config.md#tensorrt_llm_bls_model) of
+in the [model config](./docs/model_config.md#tensorrt_llm_bls-model) of
 tensorrt_llm_bls model.
 
 ### Chunked Context
 
 For more details on how to use chunked context, please refer to the
-[Chunked Context](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/advanced/gpt-attention.md#chunked-context)
+[Chunked Context](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/legacy/advanced/gpt-attention.md#chunked-context)
 section. Parameters for chunked context can be found in the
-[model config](./docs/model_config.md#tensorrt_llm_model) of tensorrt_llm model.
+[model config](./docs/model_config.md#tensorrt_llm-model) of tensorrt_llm model.
 
 ### Quantization
 
@@ -699,12 +697,10 @@ You might have to contact your cluster's administrator to help you customize the
 ## Triton Metrics
 
 Starting with the 23.11 release of Triton, users can now obtain TRT LLM Batch
-Manager [statistics](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/advanced/batch-manager.md#statistics)
-by querying the Triton metrics endpoint. This can be accomplished by launching
-a Triton server in any of the ways described above (ensuring the build code /
-container is 23.11 or later) and querying the server. Upon receiving a
-successful response, you can query the metrics endpoint by entering the
-following:
+Manager statistics by querying the Triton metrics endpoint. This can be accomplished
+by launching a Triton server in any of the ways described above (ensuring the build
+code / container is 23.11 or later) and querying the server. Upon receiving a
+successful response, you can query the metrics endpoint by entering the following:
 
 ```bash
 curl localhost:8002/metrics
@@ -803,7 +799,7 @@ Check out [GenAI-Perf](https://github.com/triton-inference-server/perf_analyzer/
 tool for benchmarking TensorRT-LLM models.
 
 You can also use the
-[benchmark_core_model script](./tools/inflight_batcher_llm/benchmark_core_model.py)
+[benchmark_core_model script](https://github.com/NVIDIA/TensorRT-LLM/blob/main/triton_backend/tools/inflight_batcher_llm/benchmark_core_model.py)
 to benchmark the core model `tensosrrt_llm`. The script sends requests directly
 to deployed `tensorrt_llm` model. The benchmark core model latency indicates the
 inference latency of TensorRT-LLM, not including the pre/post-processing latency
@@ -857,5 +853,5 @@ Expected outputs
 *Please note that the expected outputs in that document are only for reference, specific performance numbers depend on the GPU you're using.*
 
 ## Testing the TensorRT-LLM Backend
-Please follow the guide in [`ci/README.md`](ci/README.md) to see how to run
+Please follow the guide in [`tensorrt_llm/triton_backend/ci/README.md`](https://github.com/NVIDIA/TensorRT-LLM/blob/main/triton_backend/ci/README.md) to see how to run
 the testing for TensorRT-LLM backend.
diff --git a/docs/build.md b/docs/build.md
index 48d13fb4..28d8cd39 100644
--- a/docs/build.md
+++ b/docs/build.md
@@ -19,12 +19,9 @@ bash scripts/build.sh
 
 ## Build the Docker Container
 
-> [!CAUTION]
-> [build.sh](../build.sh) is currently not working and will be fixed in the next weekly update.
-
 #### Build via Docker
 
-You can build the container using the instructions in the [TensorRT-LLM Docker Build](../tensorrt_llm/docker/README.md)
+You can build the container using the instructions in the [TensorRT-LLM Docker Build](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docker/README.md)
 with `tritonrelease` stage. Please make sure to add CUDA_ARCHS flag for your GPU, for example if compute capability of your GPU is 89:
 
 ```bash
diff --git a/docs/encoder_decoder.md b/docs/encoder_decoder.md
index 77f4de42..2970a36f 100755
--- a/docs/encoder_decoder.md
+++ b/docs/encoder_decoder.md
@@ -1,7 +1,7 @@
 # End to end workflow to run an Encoder-Decoder model
 
 ### Support Matrix
-For the specific models supported by encoder-decoder family, please visit [TensorRT-LLM encoder-decoder examples](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/enc_dec#encoder-decoder-model-support). The following two model types are supported:
+For the specific models supported by encoder-decoder family, please visit [TensorRT-LLM encoder-decoder examples](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/models/core/enc_dec#encoder-decoder-model-support). The following two model types are supported:
 * T5
 * BART
 
@@ -28,7 +28,7 @@ If you're using [Triton TRT-LLM NGC container](https://catalog.ngc.nvidia.com/or
     docker run --gpus all --ipc=host --ulimit memlock=-1 --shm-size=20g `pwd`:/workspace -w /workspace nvcr.io/nvidia/tritonserver:24.08-trtllm-python-py3 bash
 ```
 
-If [building your own TensorRT-LLM Backend container](https://github.com/triton-inference-server/tensorrtllm_backend#option-2-build-via-docker) then you can run the `tensorrtllm_backend` container:
+If [building your own TensorRT-LLM Backend container](https://github.com/triton-inference-server/tensorrtllm_backend) then you can run the `tensorrtllm_backend` container:
 
 ```
     docker run --gpus all --ipc=host --ulimit memlock=-1 --shm-size=20g `pwd`:/workspace -w /workspace triton_trt_llm bash
@@ -93,7 +93,7 @@ Build TensorRT-LLM engines.
 
 > **NOTE**
 >
-> If you want to build multi-GPU engine using Tensor Parallelism then you can set `--tp_size` in convert_checkpoint.py. For example, for TP=2 on 2-GPU you can set `--tp_size=2`. If you want to use beam search then set `--max_beam_width` to higher value than 1. The `--max_input_len` in encoder trtllm-build controls the model input length and should be same as `--max_encoder_input_len` in decoder trtllm-build. Additionally, to control the model output len you should set `--max_seq_len` in decoder trtllm-build to `desired output length + 1`. It is also advisable to tune [`--max_num_tokens`](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/performance/perf-best-practices.md#max_num_tokens) as the default value of 8192 might be too large or too small depending on your input, output len and use-cases. For BART family models, make sure to remove `--context_fmha disable` from both encoder and decoder trtllm-build commands. Please refer to [TensorRT-LLM enc-dec example](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/enc_dec#build-tensorrt-engines) for more details.
+> If you want to build multi-GPU engine using Tensor Parallelism then you can set `--tp_size` in convert_checkpoint.py. For example, for TP=2 on 2-GPU you can set `--tp_size=2`. If you want to use beam search then set `--max_beam_width` to higher value than 1. The `--max_input_len` in encoder trtllm-build controls the model input length and should be same as `--max_encoder_input_len` in decoder trtllm-build. Additionally, to control the model output len you should set `--max_seq_len` in decoder trtllm-build to `desired output length + 1`. It is also advisable to tune [`--max_num_tokens`](https://github.com/NVIDIA/TensorRT-LLM/blob/v0.16.0/docs/source/performance/perf-best-practices.md#max_num_tokens) as the default value of 8192 might be too large or too small depending on your input, output len and use-cases. For BART family models, make sure to remove `--context_fmha disable` from both encoder and decoder trtllm-build commands. Please refer to [TensorRT-LLM enc-dec example](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/models/core/enc_dec#build-tensorrt-engines) for more details.
 
 #### 4. Prepare Tritonserver configs <a id="prepare-tritonserver-configs"></a>
 
diff --git a/docs/guided_decoding.md b/docs/guided_decoding.md
index 72bdb4d5..0f3205dd 100644
--- a/docs/guided_decoding.md
+++ b/docs/guided_decoding.md
@@ -2,7 +2,7 @@
 
 This document outlines the process for running guided decoding using the TensorRT-LLM backend. Guided decoding ensures that generated outputs adhere to specified formats, such as JSON. Currently, this feature is supported through the [XGrammar](https://github.com/mlc-ai/xgrammar) backend.
 
-For more information, refer to the [guided decoding documentation](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/advanced/executor.md#structured-output-with-guided-decoding) from TensorRT-LLM. Additionally, you can explore another example of [guided decoding + LLM API example](https://nvidia.github.io/TensorRT-LLM/llm-api-examples/llm_guided_decoding.html).
+For more information, refer to the [guided decoding documentation](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/legacy/advanced/executor.md#structured-output-with-guided-decoding) from TensorRT-LLM. Additionally, you can explore another example of [guided decoding + LLM API example](https://github.com/NVIDIA/TensorRT-LLM/blob/main/examples/llm-api/llm_guided_decoding.py).
 
 ## Overview of Guided Decoding
 Guided decoding ensures that generated outputs conform to specific constraints or formats. Supported guide types include:
diff --git a/docs/llama_multi_instance.md b/docs/llama_multi_instance.md
index 58cfc97d..8825e38c 100644
--- a/docs/llama_multi_instance.md
+++ b/docs/llama_multi_instance.md
@@ -1,5 +1,5 @@
 <!--
-# Copyright 2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# Copyright 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 #
 # Redistribution and use in source and binary forms, with or without
 # modification, are permitted provided that the following conditions
@@ -317,10 +317,10 @@ the model across multiple nodes.
 
 It is also possible to use orchestrator mode with MPI processes that have been
 pre-spawned. In order to do that, you need to set `--disable-spawn-processes`
-when using the [launch_triton_server.py](../scripts/launch_triton_server.py)
+when using the [launch_triton_server.py](https://github.com/NVIDIA/TensorRT-LLM/blob/main/triton_backend/scripts/launch_triton_server.py)
 script or `export TRTLLM_ORCHESTRATOR_SPAWN_PROCESSES=0`. In this mode,
 it is possible to run the server across different nodes in orchestrator mode.
 
 In order to use the orchestrator mode itself, you need to set the `--multi-model`
-flag when using the [launch_triton_server.py](../scripts/launch_triton_server.py)
+flag when using the [launch_triton_server.py](https://github.com/NVIDIA/TensorRT-LLM/blob/main/triton_backend/scripts/launch_triton_server.py)
 script or `export TRTLLM_ORCHESTRATOR=1`.
diff --git a/docs/lora.md b/docs/lora.md
index 0987c807..e1eaf3eb 100644
--- a/docs/lora.md
+++ b/docs/lora.md
@@ -1,7 +1,7 @@
 # Running LoRA inference with inflight batching
 
 Below is an example of how to run LoRA inference with inflight batching. See the
-[LoRA documentation](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/advanced/lora.md)
+[LoRA documentation](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/legacy/advanced/lora.md)
 in the TensorRT-LLM repository for more information about running gpt-2b with
 LoRA using inflight batching.
 
diff --git a/docs/model_config.md b/docs/model_config.md
index b5e05d0c..91865ef7 100644
--- a/docs/model_config.md
+++ b/docs/model_config.md
@@ -3,14 +3,14 @@
 ## Model Parameters
 
 The following tables show the parameters in the `config.pbtxt` of the models in
-[all_models/inflight_batcher_llm](../tensorrt_llm/triton_backend/all_models/inflight_batcher_llm).
+[all_models/inflight_batcher_llm](https://github.com/NVIDIA/TensorRT-LLM/tree/main/triton_backend/all_models/inflight_batcher_llm).
 that can be modified before deployment. For optimal performance or custom
 parameters, please refer to
-[perf_best_practices](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/performance/perf-best-practices.md).
+[perf_best_practices](https://github.com/NVIDIA/TensorRT-LLM/blob/v0.16.0/docs/source/performance/perf-best-practices.md).
 
 The names of the parameters listed below are the values in the `config.pbtxt`
 that can be modified using the
-[`fill_template.py`](../tensorrt_llm/triton_backend/tools/fill_template.py) script.
+[`fill_template.py`](https://github.com/NVIDIA/TensorRT-LLM/blob/main/triton_backend/tools/fill_template.py) script.
 
 **NOTE** For fields that have comma as the value (e.g. `gpu_device_ids`,
 `participant_ids`), you need to escape the comma with
@@ -350,7 +350,7 @@ Note: the timing metrics oputputs are represented as the number of nanoseconds s
 Below are some tips for configuring models for optimal performance. These
 recommendations are based on our experiments and may not apply to all use cases.
 For guidance on other parameters, please refer to the
-[perf_best_practices](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/performance/perf-best-practices.md).
+[perf_best_practices](https://github.com/NVIDIA/TensorRT-LLM/blob/v0.16.0/docs/source/performance/perf-best-practices.md).
 
 - **Setting the `instance_count` for models to better utilize inflight batching**
 
diff --git a/docs/multimodal.md b/docs/multimodal.md
index 48339db2..78646dc6 100755
--- a/docs/multimodal.md
+++ b/docs/multimodal.md
@@ -9,7 +9,7 @@ The following multimodal model is supported in tensorrtllm_backend:
 * MLLAMA
 * Qwen2-VL
 
-For more multimodal models supported in TensorRT-LLM, please visit [TensorRT-LLM multimodal examples](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/multimodal).
+For more multimodal models supported in TensorRT-LLM, please visit [TensorRT-LLM multimodal examples](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/models/core/multimodal).
 
 ## Run Multimodal with single-GPU Tritonserver
 ### Tritonserver setup steps
diff --git a/docs/whisper.md b/docs/whisper.md
index b7aabb76..ac3df27e 100644
--- a/docs/whisper.md
+++ b/docs/whisper.md
@@ -74,7 +74,7 @@ The following multimodal model is supported in tensorrtllm_backend:
 
     > **NOTE**:
     >
-    > TensorRT-LLM also supports using [distil-whisper's](https://github.com/huggingface/distil-whisper) different models by first converting their params and weights from huggingface's naming format to [openai whisper](https://github.com/openai/whisper) naming format. You can do so by running the script [distil_whisper/convert_from_distil_whisper.py](./convert_from_distil_whisper.py).
+    > TensorRT-LLM also supports using [distil-whisper's](https://github.com/huggingface/distil-whisper) different models by first converting their params and weights from huggingface's naming format to [openai whisper](https://github.com/openai/whisper) naming format. You can do so by running the script [distil_whisper/convert_from_distil_whisper.py](https://github.com/NVIDIA/TensorRT-LLM/blob/main/examples/models/core/whisper/distil_whisper/convert_from_distil_whisper.py).
 
 3. Prepare Tritonserver configs