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Achievements

Achievement: Pull Sharkx2Achievement: Pair ExtraordinaireAchievement: Quickdraw

Achievements

Achievement: Pull Sharkx2Achievement: Pair ExtraordinaireAchievement: Quickdraw

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inaniloquentee/README.md

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👨‍💻 About Me

I am a graduate student at Xi'an Jiaotong University (XJTU), focusing on AI infrastructure, LLM serving, and RL post-training systems.

I enjoy turning systems ideas into practical open-source implementations: efficient rollout execution, distributed training workflows, weight synchronization, and cross-platform GPU optimization for GRPO-style workloads.

  • 🔭 Currently contributing to: vLLM-Omni, a framework for efficient omni-modality model inference and serving.
  • 🚀 Main work: Leading Vime framework research, fork-roadmap planning, and PR delivery across CUDA and ROCm; integrating Vime with RL-Kernel for reproducible RL training and rollout.
  • 🔬 Research interests: Efficient inference, distributed attention, GRPO/RLHF systems, and cross-platform GPU performance.

🚀 Current Focus: Open Source

Project Focus Status
vLLM-Omni Efficient omni-modality model inference and serving in the vLLM ecosystem 🔥 Contributor
Vime RL framework integration, roadmap planning, and end-to-end training/rollout validation ⚡ Contributor
RL-Kernel GPU kernels and strict runtime validation consumed by Vime on CUDA and ROCm 🤝 Maintainer

🧩 Selected Systems Work

Area Selected Work
Vime framework and delivery Framework investigation, fork-version roadmap planning, upstream contribution delivery, and reproducible experiment documentation
CUDA + ROCm integration Led the Vime provider boundary across both GPU stacks, preserving Vime's loss semantics and native fallback
Distributed Attention Developed and validated paged/CP attention paths, including FlashInfer RoPE-fused attention, CP drift checks, and bitwise ROCm schedules
Deterministic runtime and performance CUDA Graph capture, tensor-parallel all-reduce optimization, strict runtime modes, and cross-configuration kernel validation
End-to-end RL validation Ran matched native/provider train-rollout consistency experiments, TP/CP ablations, bitwise checks, throughput profiling, and performance tuning on Qwen3 workloads
Provider integration Designed structured provider contracts, TP vocabulary partition handling, autograd checks, and technical write-ups for portable log-probability execution

🛠️ Tech Stack

Languages & Core AI Infrastructure Distributed & Tooling
C++
Python
CUDA		 PyTorch
vLLM
DeepSpeed		 Ray
Linux
Git

📊 GitHub Analytics


Activity Graph
Building efficient systems, one kernel and one iteration at a time.

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  1. RL-Kernel RL-Kernel Public

    Forked from RL-Align/RL-Kernel

    Modern RL Post-training Infrastructure: Optimized for NVIDIA/AMD GPUs with a focus on vLLM integration, Triton kernels, and transparent hardware-aware scaling.

    Python 2

  2. vime vime Public

    Forked from vllm-project/vime

    An LLM post-training framework with vLLM for RL Scaling

    Python

  3. DeepSpeed DeepSpeed Public

    Forked from deepspeedai/DeepSpeed

    DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

    Python

  4. Mooncake Mooncake Public

    Forked from kvcache-ai/Mooncake

    Mooncake is the serving platform for Kimi, a leading LLM service provided by Moonshot AI.

    C++

  5. vllm-omni vllm-omni Public

    Forked from vllm-project/vllm-omni

    A framework for efficient model inference with omni-modality models

    Python