gemma 4 12B it qat q4 0 uncensored heretic NVFP4

Gemma

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

Released June 2026context N/A

A community-modified variant of Gemma 4 12B with censorship filters removed, quantized to Q4_0 precision for reduced memory footprint. The quantization means it trades some output fidelity for accessibility on consumer hardware. As an uncensored derivative, it will engage with content that the base model would decline, which cuts both ways — fewer refusals, but also fewer guardrails.

Capabilities

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

Reasoning & Logic

Moderate

Coding

Moderate

Instruction Following

Use Case Fit

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

gemma 4 12B it qat q4 0 uncensored heretic NVFP4

Gemma

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

Released June 2026context N/A

A community-modified variant of Gemma 4 12B with censorship filters removed, quantized to Q4_0 precision for reduced memory footprint. The quantization means it trades some output fidelity for accessibility on consumer hardware. As an uncensored derivative, it will engage with content that the base model would decline, which cuts both ways — fewer refusals, but also fewer guardrails.

Capabilities

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

Reasoning & Logic

Moderate

Coding

Moderate

Instruction Following

Use Case Fit

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

Glossary

AccessibilityDesigning technology so people with disabilities can use it effectively.Base ModelA pretrained model that completes text patterns but hasn't been trained to follow instructions, serving as a starting point for customization through fine-tuning.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.Q4_0 PrecisionA specific quantization format that represents model weights using 4-bit integers, offering a good balance between compression and accuracy for running models on consumer hardware.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.UncensoredA model without built-in safety filters or content restrictions, allowing it to generate responses on any topic without refusal.