Gemma 4 26B A4B it Uncensored NVFP4

gemma

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released April 2026context N/A

A quantized, uncensored derivative of Google's Gemma 4 26B, published by AEON-7 with reduced safety filtering and NVIDIA FP4 precision. It trades some output guardrails for fewer refusals, which appeals to researchers and developers who need unfiltered responses. The FP4 quantization reduces memory footprint at a potential cost to output fidelity.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Reasoning & Logic

Strong

Factual Knowledge

Strong

Creative Writing

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

Gemma 4 26B A4B it Uncensored NVFP4

gemma

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released April 2026context N/A

A quantized, uncensored derivative of Google's Gemma 4 26B, published by AEON-7 with reduced safety filtering and NVIDIA FP4 precision. It trades some output guardrails for fewer refusals, which appeals to researchers and developers who need unfiltered responses. The FP4 quantization reduces memory footprint at a potential cost to output fidelity.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Reasoning & Logic

Strong

Factual Knowledge

Strong

Creative Writing

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

Glossary

FP4 PrecisionA ultra-low precision format using 4-bit floating-point numbers to represent model weights, enabling extreme compression.FP4 QuantizationA compression technique that represents model weights using only 4-bit floating-point numbers instead of larger formats, reducing memory usage and speeding up inference.FidelityThe degree to which a quantized or compressed model preserves the quality and accuracy of the original full-precision model.GuardrailsSafety mechanisms built into a model to refuse harmful requests or prevent it from generating unsafe content.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.PrecisionThe level of numerical detail a model uses to represent its internal values; higher precision means more accurate calculations but requires more memory.QuantizationReducing a model's numerical precision (e.g., from 16-bit to 4-bit) to shrink memory usage and speed up inference.QuantizedA technique that reduces a model's size and memory usage by storing weights with lower precision (fewer bits), trading some accuracy for efficiency.Safety FilteringBuilt-in guardrails in a model that prevent it from generating harmful, illegal, or unethical content by refusing certain requests.UncensoredA model without built-in safety filters or content restrictions, allowing it to generate responses on any topic without refusal.