Update README.md

README.md

@@ -24,7 +24,7 @@ KernelLLM's vision is to meet the growing demand for high-performance GPU kernel
 *KernelLLM Workflow for Triton Kernel Generation: Our approach uses KernelLLM to translate PyTorch code (green) into Triton kernel candidates. Input and output components are marked in bold. The generations are validated against unit tests, which run kernels with random inputs of known shapes. This workflow allows us to evaluate multiple generations (pass@k) by increasing the number of kernel candidate generations. The best kernel implementation is selected and returned (green output).* 
 
 
-The model was trained on approximately 25,000 paired examples of PyTorch modules and their equivalent Triton kernel implementations, and additional synthetically generated samples. Our approach combines filtered code from TheStack [Kocetkov et al. 2022] and synthetic examples generated through `torch.compile()` and additional prompting techniques. The filtered and compiled dataset can be found [on Huggingface](https://huggingface.co/datasets/GPUMODE/Inductor_Created_Data_Permissive).
+The model was trained on approximately 25,000 paired examples of PyTorch modules and their equivalent Triton kernel implementations, and additional synthetically generated samples. Our approach combines filtered code from TheStack [Kocetkov et al. 2022] and synthetic examples generated through `torch.compile()` and additional prompting techniques. The filtered and compiled dataset can be found [on the GPU MODE Huggingface](https://huggingface.co/datasets/GPUMODE/Inductor_Created_Data_Permissive).
 
 We finetuned Llama3.1-8B-Instruct on the created dataset using supervised instruction tuning and measured its ability to generate correct Triton kernels and corresponding calling code on KernelBench-Triton, our newly created variant of KernelBench [Ouyang et al. 2025] targeting Triton kernel generation. The torch code was used with a prompt template containing a format example as instruction during both training and evaluation. The model was trained for 10 epochs with a batch size of 32 and a standard SFT recipe with hyperparameters selected by perplexity on a held-out subset of the training data. Training took circa 12 hours wall clock time on 16 GPUs (192 GPU hours), and we report the best checkpoint's validation results.
 
@@ -173,4 +173,8 @@ Despite showing promising results, KernelLLM has several limitations:
 
 KernelLLM and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, KernelLLM's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of KernelLLM, developers should perform safety testing and tuning tailored to their specific applications of the model.
 
-Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide](https://ai.meta.com/llama/responsible-use-guide).
+Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide](https://ai.meta.com/llama/responsible-use-guide).
+
+## Acknowledgements 
+
+KernelLLM would not be possible if it was not for our close collaboration with [Simon Guo](https://simonguo.tech/) and [Simran Arora])(https://arorasimran.com/) from Stanford and [Alex Zhang](https://alexzhang13.github.io/). To learn more about how KernelLLM was a community project, you can watch our talks at [GPU MODE @ GTC](https://www.youtube.com/watch?v=FtgXueoQkA0)