fix broken links

Signed-off-by: Andrew Chen <[email protected]>

Author: chenopis

docs/source/advanced/expert-parallelism.md

@@ -4,7 +4,7 @@
 
 ## Mixture of Experts (MoE)
 
-Mixture of Experts (MoE) architectures have been used widely recently, such as [Mistral Mixtral 8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1/blob/main/config.json). Specifically, MOE’s structure supports multiple parallel Feedforward Neural Network (FFN) layers (called experts) to replace the single FFN layer in the dense model. When tokens arrive, the router layer selects the TopK experts for each token. The corresponding hidden state of the token is then dispatched to the selected TopK experts, respectively. As a result, there are multiple tokens’ hidden states that are dispatched to each expert.
+Mixture of Experts (MoE) architectures have been used widely recently, such as [Mistral Mixtral 8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1). Specifically, MOE’s structure supports multiple parallel Feedforward Neural Network (FFN) layers (called experts) to replace the single FFN layer in the dense model. When tokens arrive, the router layer selects the TopK experts for each token. The corresponding hidden state of the token is then dispatched to the selected TopK experts, respectively. As a result, there are multiple tokens’ hidden states that are dispatched to each expert.
 
 <img src="https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/blogs/media/moe_structure.png?raw=true" alt="moe_structure" width="500" height="auto">
 

docs/source/advanced/speculative-decoding.md

@@ -60,7 +60,7 @@ These tokens are then forwarded to the Target model for verification.
 Upon verification, the Target model may return up to `K+1` tokens.
 Subsequently, the prompt, now updated with the accepted tokens, is sent back to the Draft model to initiate the generation of new draft tokens.
 This iterative process continues until a predefined stop conditions are met.
-An example of this orchestration process can be found in the [TensorRT-LLM Triton backend](https://github.com/triton-inference-server/tensorrtllm_backend/blob/main/inflight_batcher_llm/client/e2e_grpc_speculative_decoding_client.py).
+An example of this orchestration process can be found in the [TensorRT-LLM Triton backend](https://github.com/triton-inference-server/tensorrtllm_backend).
 
 We provide two styles of running Draft-Target-Model now: using TensorRT-LLM-BLS in Triton Inference Server, or using TensorRT-LLM directly. Detailed steps of running can be found in [examples/draft_target_model/README.md](https://github.com/NVIDIA/TensorRT-LLM/blob/main/examples/draft_target_model/README.md) and the code can be found in [examples/ngram/run_dtm_ngram.py](https://github.com/NVIDIA/TensorRT-LLM/blob/main/examples/ngram/run_dtm_ngram.py).
 
@@ -172,7 +172,7 @@ Similarly to ReDrafter, TensorRT-LLM implements the EAGLE model such that logits
 
 ### Disaggregated Serving
 
-[Disaggregated Serving](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/advanced/disaggregated-service.md) with EAGLE3 using the two model approach is supported in the Pytorch backend. Please refer to the following [Dynamo example](https://github.com/ai-dynamo/dynamo/blob/main/examples/tensorrt_llm/llama4_plus_eagle.md) on how to run EAGLE3 with Disaggregated Serving for Llama 4 Maverick.
+[Disaggregated Serving](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/advanced/disaggregated-service.md) with EAGLE3 using the two model approach is supported in the Pytorch backend.
 
 ## Lookahead Decoding
 

docs/source/blogs/Falcon180B-H200.md

@@ -33,7 +33,7 @@ Often quantization can have adverse impacts on the accuracy of the model,
 however, TensorRT-LLM's AWQ decreases memory footprint of the model by **4x**
 while maintaining high accuracy.
 
-<img src="https://github.com/NVIDIA/TensorRT-LLM/blob/rel/docs/source/blogs/media/Falcon180B-H200_acc.png?raw=true" alt="Falcon-180B accuracy comparison" width="600" height="auto">
+<img src="https://github.com/NVIDIA/TensorRT-LLM/blob/5aec7af45fc0abd876fa68a9ae8c8cae084f3af3/docs/source/blogs/media/Falcon180B-H200_acc.png" alt="Falcon-180B accuracy comparison" width="600" height="auto">
 
 
 <sup>Preliminary measured accuracy, subject to change. </sup>

docs/source/blogs/tech_blog/blog1_Pushing_Latency_Boundaries_Optimizing_DeepSeek-R1_Performance_on_NVIDIA_B200_GPUs.md

@@ -125,7 +125,7 @@ The modules in the diagram are:
 | Baseline: CUDA Graph + EP8TP8                             |   67     | [modeling_deepseekv3.py](https://github.com/NVIDIA/TensorRT-LLM/blob/main/tensorrt_llm/_torch/models/modeling_deepseekv3.py)                                |
 | Multi Stream to overlap shared expert with sparse experts |   73     | [modeling_deepseekv3.py#L506](https://github.com/NVIDIA/TensorRT-LLM/blob/14bfb5e0d6e81aec3306a1324cf074566646f886/tensorrt_llm/_torch/models/modeling_deepseekv3.py#L506) |
 | Optimize MLA Kernel                                       |   80     | [PR #3763](https://github.com/NVIDIA/TensorRT-LLM/pull/3763)                                                                                                |
-| Optimize TopK Kernels                                     |   84     | • [RoutingKernel.cu](https://github.com/NVIDIA/TensorRT-LLM/blob/main/cpp/tensorrt_llm/kernels/trtllmGenKernels/blockScaleMoe/trtllmGenSrc/RoutingKernel.cu)<br/>• [noAuxTcKernels.cu](https://github.com/NVIDIA/TensorRT-LLM/blob/main/cpp/tensorrt_llm/kernels/noAuxTcKernels.cu) |
+| Optimize TopK Kernels                                     |   84     | • [RoutingKernelTopK.cuh](https://github.com/NVIDIA/TensorRT-LLM/blob/main/cpp/tensorrt_llm/kernels/trtllmGenKernels/blockScaleMoe/RoutingKernelTopK.cuh)<br/>• [noAuxTcKernels.cu](https://github.com/NVIDIA/TensorRT-LLM/blob/main/cpp/tensorrt_llm/kernels/noAuxTcKernels.cu) |
 | Optimize Fuse_A_GEMM                                      |   89     | [attention.py#L345](https://github.com/NVIDIA/TensorRT-LLM/blob/d6b741ddfe7f8a80718c10d49773c42abc0a254f/tensorrt_llm/_torch/modules/attention.py#L345)     |
 | MTP3_Vanilla                                              |   154    | evolve to MTP3_Autoregressive                                                                                                                                                           |
 | Evolve to MTP3_Autoregressive + Optimize Router GEMM      |   164    | [modeling_deepseekv3.py#L304](https://github.com/NVIDIA/TensorRT-LLM/blob/d6b741ddfe7f8a80718c10d49773c42abc0a254f/tensorrt_llm/_torch/models/modeling_deepseekv3.py#L304) |

docs/source/performance/perf-benchmarking.md

@@ -79,7 +79,7 @@ that have been validated extensively and is the same listing as seen on the
 - [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)
 - [meta-llama/Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct)
 - [meta-llama/Llama-3.1-405B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct)
-- [mistralai/Mixtral-8x7B-v0.1-Instruct](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1-Instruct)
+- [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)
 
 ```{tip}
 `trtllm-bench` can automatically download the model from Hugging Face Model Hub.

docs/source/release-notes.md

@@ -639,7 +639,7 @@ All published functionality in the Release Notes has been fully tested and verif
 
 ### Known Issues
 
-- On Windows, installation of TensorRT-LLM may succeed, but you might hit `OSError: exception: access violation reading 0x0000000000000000` when importing the library in Python. See [Installing on Windows](https://nvidia.github.io/TensorRT-LLM/installation/windows.html) for workarounds.
+- On Windows, installation of TensorRT-LLM may succeed, but you might hit `OSError: exception: access violation reading 0x0000000000000000` when importing the library in Python.
 
 
 ## TensorRT-LLM Release 0.11.0
@@ -1045,7 +1045,7 @@ Refer to the {ref}`support-matrix-software` section for a list of supported mode
 - System prompt caching
 - Enabled split-k for weight-only cutlass kernels
 - FP8 KV cache support for XQA kernel
-- New Python builder API and `trtllm-build` command (already applied to [blip2](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/blip2) and [OPT](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/opt#3-build-tensorrt-engines))
+- New Python builder API and `trtllm-build` command and OPT
 - Support `StoppingCriteria` and `LogitsProcessor` in Python generate API
 - FHMA support for chunked attention and paged KV cache
 - Performance enhancements include:

docs/source/torch.md

@@ -29,7 +29,6 @@ Here is a simple example to show how to use `tensorrt_llm.LLM` API with Llama mo
 
 - [Architecture Overview](./torch/arch_overview.md)
 - [Adding a New Model](./torch/adding_new_model.md)
-- [Examples](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/pytorch/README.md)
 
 ## Key Components