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...It has been benchmarked on HumanEval-X, a multilingual program synthesis benchmark introduced alongside the model, and achieves state-of-the-art performance compared to other open models like InCoder and CodeGen. CodeGeeX also powers IDE plugins for VS Code and JetBrains, offering features like code completion, translation, debugging, and annotation. The model supports Ascend 910 and NVIDIA GPUs, with optimizations like quantization and FasterTransformer acceleration for faster inference.

FlashMLA is a high-performance decoding kernel library designed especially for Multi-Head Latent Attention (MLA) workloads, targeting NVIDIA Hopper GPU architectures. It provides optimized kernels for MLA decoding, including support for variable-length sequences, helping reduce latency and increase throughput in model inference systems using that attention style. The library supports both BF16 and FP16 data types, and includes a paged KV cache implementation with a block size of 64 to...

...The environments are designed to run very quickly (thousands of steps per second on a single core) to facilitate large-scale experiments and make benchmarking efficient. The benchmark supports both “easy” and “hard” difficulty modes, letting researchers trade off computational cost vs challenge. The repo provides a C++ core for game logic and rendering (with support for gym/Gym3 wrappers) plus Python bindings and interactive mode for human play testing.