cudaLLM-8B/README.md

license, datasets, base_model, pipeline_tag, tags

license	datasets	base_model	pipeline_tag	tags
apache-2.0	ByteDance-Seed/cudaLLM-data	Qwen/Qwen3-8B	text-generation	code CUDA

CudaLLM: A Language Model for High-Performance CUDA Kernel Generation

Model Description

cudaLLM-8B is a language model for generating high-performance and syntactically correct CUDA kernels. It is based on the Qwen3-8B model and has undergone a two-stage training process to master the complexities of parallel programming for GPUs.

Performance on KernelBench:

	Bo1	Bo2	Bo4	Bo8	Bo16
Level-1	79.75	83	84	86	87
Level-2	67.30	70	71	72	73
Level-3	20.83	26	30	34	36

Training Procedure

The model was trained using the verl library. The model was trained and evaluated on:

• SFT Dataset: A high-quality dataset of CUDA problem-solution pairs (sft_cuda_llm_r1.parquet), originally generated by DeepSeek R1, DeepSeel Coder-7B, and Qwen2-32B.
• RL Dataset: A refined dataset (rl_cuda_llm_0424.parquet) used to provide performance-based rewards during the RL stage.
• Evaluation Dataset: The model's performance was benchmarked against the KernelBench dataset.

Intended Use and Limitations

Intended Use

The primary use of CudaLLM is to assist developers in writing and optimizing high-performance CUDA kernels. It can be used for:

• Accelerating scientific computing and machine learning workloads.
• As a co-pilot or productivity tool for HPC and CUDA developers.
• Research into AI-driven code generation and optimization.

Limitations and Bias

• Correctness is Not Guaranteed: While trained to produce correct code, the model's output should always be rigorously tested and verified before deployment in production systems.
• Security Risks: The generated code is not guaranteed to be secure. Never run model-generated code from an untrusted source without careful inspection.
• Performance Variability: Kernel performance can vary significantly depending on the target GPU architecture, input data sizes, and compiler version. The generated code may require further manual tuning.
• Specialized Domain: This model is highly specialized for CUDA code generation. Its performance on general-purpose programming tasks or natural language conversation will be limited.