language, pipeline_tag, tags, license, library_name

language	pipeline_tag	tags	license	library_name
en	text-generation	moe alignment	apache-2.0	transformers

MoE-Pilot-Align-2.7B

Overview

MoE-Pilot-Align-2.7B is a high-performance Sparse Mixture-of-Experts (MoE) model (14.3B total / 2.7B active), meticulously adapted and fine-tuned on a standard 8-GPU SXM node.

This project is not a conventional fine-tuning exercise; it is an Infrastructure-Level Alignment showcase. It validates a production-ready pipeline for scaling MoE architectures by resolving critical bottlenecks in distributed initialization, heterogeneous memory management, and collective communication.

The Engine: Kernel-Align

To achieve maximum throughput and memory efficiency during the alignment phase, this model is powered by Kernel-Align, an extreme post-training infrastructure optimized for NVIDIA and AMD GPUs.

• Breaking the Memory Wall: Optimized for GRPO (Group Relative Policy Optimization), enabling a Group Size of G=256 on a single A100 by keeping additional VRAM usage constant (~0.5GB).
• Extreme Sampling Latency: Integrated with FlashInfer and custom fused kernels, achieving up to 399x speedup in the rollout phase compared to native PyTorch.
• Universal Backend: Native support for AMD (ROCm/AITER) and NVIDIA (CUDA), ensuring seamless cross-platform performance.

Engineering & Infrastructure Innovations

1. Zero-Deadlock AOT Kernel Orchestration

In high-density 8-GPU environments, simultaneous JIT (Just-in-Time) compilation of MoE fused kernels often triggers distributed deadlocks due to I/O race conditions during the NCCL handshake.

• Innovation: Implemented an Ahead-of-Time compilation orchestrator. By decoupling CUDA/ROCm kernel building from the training lifecycle, we achieved zero-latency "Cold Starts" and eliminated synchronization timeouts.

2. Reflection-Based Parameter Metadata Injection

Standard DeepSpeed-ZeRO engines require precise expert-group tagging to avoid AssertionError during state partitioning. Hard-coded tagging is fragile and lacks portability.

• Solution: Developed a Reflection-based Metadata Injector. At the optimizer setup stage, the system dynamically scans tensors for allreduce attributes and naming patterns to inject moe: True tags.
• Impact: Ensured seamless compatibility between the MoE expert layers and the ZeRO-1/2 optimizer state sharding, maximizing VRAM efficiency on the 80GB A100 footprint.

3. Topology-Aware Parallelism (EP8)

• Parallelism Strategy: Optimized for a Single-Node 8-GPU Full-Mesh topology using Expert Parallelism (EP=8).
• Communication Tuning: Refactored the All-to-All collective primitives to match the NVLink 3.0 bi-directional bandwidth. By balancing the load across 32 total experts (4 experts per GPU), we maintained a stable throughput of 185+ TFLOPs per device.

Model Specifications

Feature	Configuration
Architecture	Sparse MoE (Decoder-only)
Total Parameters	14.3 Billion
Active Parameters	2.7 Billion
Experts Count	32 (Total)
Routing Strategy	Top-2 Gating with Load Balancing
Precision	Mixed Precision (BF16-O2)

Hardware & Software Environment

• Compute: 1 x Node | 8 x NVIDIA A100 80GB SXM
• Interconnect: NVLink 3.0 (600 GB/s Full-Mesh)
• Framework: Custom Fork of Megatron-DeepSpeed
• CUDA/ROCm Compatibility: Validated on CUDA 12.4; Architectural design supports seamless porting to AMD MI300X/ROCm environments via RCCL optimization.

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Community Quantizations

Special thanks to @mradermacher for providing GGUF weights:

• GGUF Weights (Q2_K to Q8_0)

This repository serves as a technical benchmark for implementing production-scale MoE alignment protocols on next-generation heterogeneous compute clusters.