Model Detail
MiniMax-M2.7-JANG_2L
—MiniMax-M2.7-JANG_2L is a code generation model with 9.3B parameters released by JANGQ-AI. The model is registered under the text-generation pipeline tag on Hugging Face, distributed under a other license.
MiniMax-M2.7-JANG_2L ships with 9.3B parameters. Total weight footprint is approximately 18.6 GB, which is the relevant figure when planning local-inference VRAM. Distribution is governed by the other license — review the exact terms before commercial deployment.
MiniMax-M2.7-JANG_2L is best fit for code completion, repository-scale Q&A, and pair-programming integrations. It is a less obvious choice for one-shot generation of security-critical code without review. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
Minimax-optimal Scaling Laws in Ridge Regression via Transfer Learning
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When Is Muon Optimal? Radial Fragility and Minimax Geometry
arXiv:2610.21549v1 Announce Type: cross Abstract: Muon replaces a matrix gradient by an approximate polar factor and has shown strong empirical performance in neural-network training. The Isotropic Curvature Model (ICM) provides a curvature-based explanation for this update by asking when gradient o
Two Sample Sizes in Test-Time LLM Ensembling: Buffered ERM and Minimax Theory
arXiv:2610.21363v1 Announce Type: cross Abstract: Test-time scaling can improve the performance of large language models by generating multiple answers to a question and aggregating them. Weighted ensembles extend this idea by drawing answers from several models, with the weights determining how oft
Discrete Diffusion Models are Minimax Optimal Under Sparsity
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Variance-Reduced Policy Gradient is Nearly Minimax-Optimal
arXiv:2610.13692v1 Announce Type: cross Abstract: Standard Policy Gradient (PG) algorithms suffer from high estimation variance because they rely on Monte Carlo updates. While off-policy variance reduction mitigates this problem, establishing optimal sample complexity currently requires either restr
