prithivMLmods/gemma-4-31B-it-Uncensored-MAX

VISIONConcurrent Unit Cost:2Model Size:31BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Apr 4, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Featherless Exclusive Cold

prithivMLmods/gemma-4-31B-it-Uncensored-MAX is a 31 billion parameter instruction-tuned language model built on the Gemma-4 architecture, optimized for improved compatibility with the latest Transformers releases. This version focuses on updated shard sizing, repository optimization, and enhanced deployment stability, while preserving the strong reasoning and instruction-following capabilities of its base model. It is designed for stable inference and efficient integration into modern AI ecosystems, making it suitable for research, red-teaming, and high-performance deployment scenarios.

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Overview

gemma-4-31B-it-Uncensored-MAX is a 31 billion parameter instruction-tuned language model, an optimized release built upon huihui-ai/Huihui-gemma-4-31B-it-abliterated. This version prioritizes updated shard sizing, repository optimization, and enhanced compatibility with the latest Transformers releases. It maintains the robust reasoning and instruction-following strengths inherent to the Gemma architecture, offering a powerful model designed for stable inference, efficient deployment, and seamless integration into modern AI development.

Key Capabilities

  • Latest Transformers Compatibility: Re-sharded and optimized for improved compatibility with recent Transformers library versions.
  • Optimized Model Sharding: Features an updated shard structure for better storage handling, download reliability, and inference efficiency.
  • Stable Inference Pipeline: Provides improved packaging for consistent loading and generation behavior.
  • Preserved Model Behavior: No modifications to weights or architecture, ensuring behavior consistent with the base model lineage.

Good for

  • Multimodal and Language Research: Ideal for studying large-scale transformer behavior and inference characteristics.
  • Red-Teaming & Evaluation: Suitable for testing model robustness against challenging prompts and edge cases.
  • High-Performance Deployment: Designed for running large models on optimized GPU or distributed inference setups.
  • Research Prototyping: Useful for experimentation with scalable transformer architectures.