The Multimodal Model Battlefield Is Going Rogue – LoongForge’s ‘Dark Arts’ Framework

The current large‑model landscape is dominated by LLMs, which are likened to a basic internal skill; frameworks such as Megatron serve as powerful "Nine‑Yang" techniques for massive distributed LLM training.

However, focusing solely on LLM "internal skills" is no longer sufficient; practitioners now pursue multimodal mastery (LLM + VLM + VLA + Diffusion), which introduces four major obstacles: (1) a proliferation of modalities and scenarios, (2) disparate component parameter scales (e.g., ViT vs. LLM), (3) heterogeneous data sequences causing load imbalance, and (4) incompatibility across diverse hardware platforms.

To address these issues, Baidu Baige proposes a unified multimodal training methodology dubbed “Qian Kun Da Nuo Yi”. Its core tenets are threefold: Unified , Efficient , and Easy‑to‑Use . The framework tackles model management, flexible component composition, efficient data allocation, cross‑hardware code portability, and automated training‑strategy scheduling.

The solution abstracts models into three layers: Encoder (perception layer) , Foundation (generation backbone) , and OminiCombinationModel (combination‑scheduling layer) . New modalities are integrated simply by registering the appropriate components, eliminating the need for low‑level code changes.

Performance is boosted through a three‑stage optimization pipeline: (1) LLM‑base optimizations (the “Ren” channel), (2) multimodal‑specific refinements (the “Du” channel), and (3) low‑level operator acceleration (the “Chong” channel). This “Ren‑Du‑Chong” approach dramatically shortens training time and reduces resource occupancy.

Benchmark results on production clusters show: DSA‑style models (DeepSeek V3.2, GLM‑5, etc.) achieve >5× performance gains; mainstream VLMs (Qwen‑3 series) see 1.15‑1.45× end‑to‑end acceleration; embodied‑intelligence models (GR00T N1.6, Pi0.5) obtain 1.65‑2.31× speedups; and on a 5,000‑card Kunlun XPU P800 cluster, linear scaling exceeds 90 %.

The framework natively supports a wide range of models—including DeepSeek, Qwen, InternVL, LLaVA‑OV, ERNIE, MiniMax, MIMO, Pi0.5, GR00T N1.6, WAN—and runs seamlessly on both GPU and Kunlun XPU platforms.

LoongForge is released under an open‑source license; its source code and documentation are available at https://github.com/baidu-baige/LoongForge and https://baidu-baige.github.io/LoongForge. The open‑source release aims to democratize multimodal training, allowing the entire community to adopt the “dark arts” of unified model training.