Model Detail
MiniMax-M2.7-NVFP4
—MiniMax-M2.7-NVFP4 is a code generation model with 65.2B parameters released by lukealonso. Distributed under the permissive mit license.
MiniMax-M2.7-NVFP4 ships with 65.2B parameters. Total weight footprint is approximately 130.4 GB, which is the relevant figure when planning local-inference VRAM. The mit license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
MiniMax-M2.7-NVFP4 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.
Black-Box Assisted Regression: Phase Transitions and Minimax Optimality
arXiv:2606.25743v1 Announce Type: new Abstract: Foundation models are often used as fixed black-box predictors for downstream tasks with limited labeled data, but their predictions may be biased and unsafe to trust blindly. We study this setting through black-box assisted nonparametric regression: a
Minimax PAC Bounds for Learning in Exogenous Contextual MDPs
arXiv:2606.25170v1 Announce Type: cross Abstract: We study PAC learning in tabular discounted Markov decision processes with exogenous i.i.d. contexts, with discount factor $\gamma$, finite state space $\mathcal X$, action space $\mathcal A$, and context space $\mathcal Z$. At each time step, a cont
Quantile of Means: A Bonus-Free Ensemble Method for Minimax Optimal Reinforcement Learning
arXiv:2606.20107v1 Announce Type: new Abstract: Optimal Reinforcement Learning (RL) algorithms typically rely on carefully constructed count-based uncertainty estimates to drive exploration. Although theoretically sound, such estimates are hard to compute in practical settings and therefore offer li
Learning from Biased and Costly Data Sources: Minimax-optimal Data Collection under a Budget
arXiv:2602.17894v2 Announce Type: replace-cross Abstract: Data collection is a critical component of modern statistical and machine learning pipelines, particularly when data must be gathered from multiple heterogeneous sources to study a target population of interest. In many use cases, such as med
Enhancing LLM Safety Through a Theoretical Minimax Game Lens
arXiv:2502.05163v2 Announce Type: replace Abstract: The rapid advancement of large language models (LLMs) necessitates effective mechanisms to ensure their responsible deployment by accurately distinguishing unsafe content from benign content. While substantial safety datasets are available in Engli
MiniMax Sparse Attention
arXiv:2606.13392v2 Announce Type: replace Abstract: Ultra-long-context capability is becoming indispensable for frontier LLMs: agentic workflows, repository-scale code reasoning, and persistent memory all require the model to jointly attend over hundreds of thousands to millions of tokens, yet the q
