From 368cdfbe2f29a22b0f71e21023984c63db085777 Mon Sep 17 00:00:00 2001
From: amote-i <49533125+amote-i@users.noreply.github.com>
Date: Tue, 14 Apr 2026 19:21:15 +0800
Subject: [PATCH] [NPU] [DOC] Fix outdated descriptions in the NPU
 documentation (#22707)

---
 docs/platforms/ascend/ascend_contribution_guide.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/platforms/ascend/ascend_contribution_guide.md b/docs/platforms/ascend/ascend_contribution_guide.md
index e25cb9f42..4d3ad0d3a 100644
--- a/docs/platforms/ascend/ascend_contribution_guide.md
+++ b/docs/platforms/ascend/ascend_contribution_guide.md
@@ -73,7 +73,7 @@ Also, do not rely on the "Latency/Output throughput" from this script, as it is
 GSM8K is too easy for state-of-the-art models nowadays. Please try your own more challenging accuracy tests.
 You can find additional accuracy eval examples in:
 - [test_eval_accuracy_large.py](https://github.com/sgl-project/sglang/blob/main/test/registered/eval/test_eval_accuracy_large.py)
-- [test_moe_eval_accuracy_large.py](https://github.com/sgl-project/sglang/blob/main/test/registered/eval/test_moe_eval_accuracy_large.py)
+- [test_gpt_oss_1gpu.py](https://github.com/sgl-project/sglang/blob/main/test/registered/core/test_gpt_oss_1gpu.py)
 
 ## Benchmark the speed
 Refer to [Benchmark and Profiling](../../developer_guide/benchmark_and_profiling.md).
@@ -113,7 +113,7 @@ Each CI workflow has a default limit defined in its workflow configuration file.
 
 ```yaml
 cool-down-minutes:
-  description: "Default cooldown period in minutes; 0 disables rate limiting"
+  description: "Cooldown period in minutes for low-permission users; 0 disables rate limiting"
   type: number
   default: 120
 ```
@@ -133,7 +133,7 @@ Users listed in [CI_PERMISSIONS.json](https://github.com/sgl-project/sglang/blob
   - Reuse server launches in your unit tests to make tests run faster.
 - When supporting new hardware or features, follow these guidelines:
   - Do not drastically change existing code.
-  - Always prefer new files to introduce specific components for your new hardware (e.g., `allocator_ascend.py`).
+  - Always prefer new files to introduce specific components for your new hardware (e.g., `allocator_npu.py`).
   - If you write multiple if/else blocks for new features, ensure the common path (e.g., NVIDIA hardware or the existing code path) is the first branch.
 
 ## How to update sgl-kernel