[Doc] links Tensorizer example

docs/source/getting_started/installation/gpu-rocm.md

@@ -106,9 +106,9 @@ $ cd ../..
 - If you see HTTP issue related to downloading packages during building triton, please try again as the HTTP error is intermittent.
 ```
 
-2. Optionally, if you choose to use CK flash attention, you can install [flash attention for ROCm](https://github.com/ROCm/flash-attention/tree/ck_tile)
+2. Optionally, if you choose to use CK flash attention, you can install [flash attention for ROCm](https://github.com/ROCm/flash-attention/tree/b7d29fb)
 
-Install ROCm's flash attention (v2.5.9.post1) following the instructions from [ROCm/flash-attention](https://github.com/ROCm/flash-attention/tree/ck_tile#amd-gpurocm-support)
+Install ROCm's flash attention (v2.7.0-cktile) following the instructions from [ROCm/flash-attention](https://github.com/ROCm/flash-attention/tree/b7d29fb#amd-rocm-support)
 Alternatively, wheels intended for vLLM use can be accessed under the releases.
 
 For example, for ROCm 6.2, suppose your gfx arch is `gfx90a`. To get your gfx architecture, run `rocminfo |grep gfx`.

docs/source/models/extensions/tensorizer.md

@@ -9,7 +9,7 @@ shorter Pod startup times and CPU memory usage. Tensor encryption is also suppor
 
 For more information on CoreWeave's Tensorizer, please refer to
 [CoreWeave's Tensorizer documentation](https://github.com/coreweave/tensorizer). For more information on serializing a vLLM model, as well a general usage guide to using Tensorizer with vLLM, see
-the [vLLM example script](https://docs.vllm.ai/en/stable/getting_started/examples/offline_inference/tensorize_vllm_model.html).
+the [vLLM example script](gh-file:examples/offline_inference/tensorize_vllm_model.py).
 
 ```{note}
 Note that to use this feature you will need to install `tensorizer` by running `pip install vllm[tensorizer]`.

docs/source/models/supported_models.md

@@ -843,5 +843,5 @@ We have the following levels of testing for models:
 
 1. **Strict Consistency**: We compare the output of the model with the output of the model in the HuggingFace Transformers library under greedy decoding. This is the most stringent test. Please refer to [models tests](https://github.com/vllm-project/vllm/blob/main/tests/models) for the models that have passed this test.
 2. **Output Sensibility**: We check if the output of the model is sensible and coherent, by measuring the perplexity of the output and checking for any obvious errors. This is a less stringent test.
-3. **Runtime Functionality**: We check if the model can be loaded and run without errors. This is the least stringent test. Please refer to [functionality tests](gh-dir:tests) and [examples](gh-dir:main/examples) for the models that have passed this test.
+3. **Runtime Functionality**: We check if the model can be loaded and run without errors. This is the least stringent test. Please refer to [functionality tests](gh-dir:tests) and [examples](gh-dir:examples) for the models that have passed this test.
 4. **Community Feedback**: We rely on the community to provide feedback on the models. If a model is broken or not working as expected, we encourage users to raise issues to report it or open pull requests to fix it. The rest of the models fall under this category.