[None][doc] Add the missing content for model support section and fix valid links for long_sequence.md (#8869)

Signed-off-by: nv-guomingz <[email protected]>

Author: nv-guomingz

docs/source/features/long-sequence.md

@@ -26,7 +26,7 @@ Note that if chunked context is enabled, please set the `max_num_tokens` to be a
 
 <div align="center">
 <figure>
-  <img src="https://github.com/NVIDIA/TensorRT-LLM/raw/main/docs/source/blogs/media/feat_long_seq_chunked_attention.png" alt="feat_long_seq_chunked_attention" width="240" height="auto">
+  <img src="https://github.com/NVIDIA/TensorRT-LLM/raw/main/docs/source/media/feat_long_seq_chunked_attention.png" alt="feat_long_seq_chunked_attention" width="240" height="auto">
 </figure>
 </div>
 <p align="center"><sub><em>Figure 1. Illustration of chunked attention </em></sub></p>
@@ -43,7 +43,7 @@ Note that chunked attention can only be applied to context requests.
 
 <div align="center">
 <figure>
-  <img src="https://github.com/NVIDIA/TensorRT-LLM/raw/main/docs/source/blogs/media/feat_long_seq_chunked_attention.png" alt="feat_long_seq_sliding_win_attn" width="240" height="auto">
+  <img src="https://github.com/NVIDIA/TensorRT-LLM/raw/main/docs/source/media/feat_long_seq_chunked_attention.png" alt="feat_long_seq_sliding_win_attn" width="240" height="auto">
 </figure>
 </div>
 <p align="center"><sub><em>Figure 2. Illustration of sliding window attention </em></sub></p>

docs/source/overview.md

@@ -25,6 +25,11 @@ TensorRT LLM delivers breakthrough performance on the latest NVIDIA GPUs:
 
 TensorRT LLM supports the latest and most popular LLM architectures:
 
+- **Language Models**: GPT-OSS, Deepseek-R1/V3, Llama 3/4, Qwen2/3, Gemma 3, Phi 4...
+- **Multi-modal Models**: LLaVA-NeXT, Qwen2-VL, VILA, Llama 3.2 Vision...
+
+TensorRT LLM strives to support the most popular models on **Day 0**.
+
 ### FP4 Support
 [NVIDIA B200 GPUs](https://www.nvidia.com/en-us/data-center/dgx-b200/) , when used with TensorRT LLM, enable seamless loading of model weights in the new [FP4 format](https://developer.nvidia.com/blog/introducing-nvfp4-for-efficient-and-accurate-low-precision-inference/#what_is_nvfp4), allowing you to automatically leverage optimized FP4 kernels for efficient and accurate low-precision inference.