AscendKernelGen commited on
Commit
2bfaa94
·
verified ·
1 Parent(s): 32ea34d

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +0 -2
README.md CHANGED
@@ -12,8 +12,6 @@ language:
12
  KernelGen-LM-32B is a state-of-the-art domain-adaptive large language model specialized for low-level NPU kernel generation, specifically for the Huawei Ascend architecture using the AscendC programming language. Built upon the Qwen3-32B backbone, it is trained on the Ascend-CoT dataset and refined via reinforcement learning with execution feedback. It achieves unprecedented success rates in generating complex, functional hardware kernels, improving compilation success on L2 tasks from 0% (baseline) to 96.5% (Pass@10), while functional correctness achieves
13
  40.5% compared to the baseline’s complete failure.
14
 
15
- The AscendKernelGen Technical Report is published at https://arxiv.org/abs/2601.07160.
16
-
17
  **Other artifacts:**
18
  * The **AscendKernelGen Technical Report** is published at https://arxiv.org/abs/2601.07160.
19
  * The **NPUKernelBench** evaluation framework is published at https://git.openi.org.cn/PCL-Benchmark/NPUKernelBench.
 
12
  KernelGen-LM-32B is a state-of-the-art domain-adaptive large language model specialized for low-level NPU kernel generation, specifically for the Huawei Ascend architecture using the AscendC programming language. Built upon the Qwen3-32B backbone, it is trained on the Ascend-CoT dataset and refined via reinforcement learning with execution feedback. It achieves unprecedented success rates in generating complex, functional hardware kernels, improving compilation success on L2 tasks from 0% (baseline) to 96.5% (Pass@10), while functional correctness achieves
13
  40.5% compared to the baseline’s complete failure.
14
 
 
 
15
  **Other artifacts:**
16
  * The **AscendKernelGen Technical Report** is published at https://arxiv.org/abs/2601.07160.
17
  * The **NPUKernelBench** evaluation framework is published at https://git.openi.org.cn/PCL-Benchmark/NPUKernelBench.