Huihui gemma 4 26B A4B it abliterated

Gemma

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

Released April 2026context N/A26B params

A community-modified version of Gemma 4 26B that has undergone 'abliteration' — a fine-tuning technique that removes built-in refusal behaviors, making it more willing to respond to prompts that the base model would typically decline. With only ~4B parameters active at inference time despite a 26B total parameter count, it runs efficiently. Trade-off: safety guardrails are intentionally removed.

Capabilities

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

Creative Writing

Strong

Instruction Following

Strong

Factual Knowledge

Use Case Fit

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

Huihui gemma 4 26B A4B it abliterated

Gemma

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

Released April 2026context N/A26B params

A community-modified version of Gemma 4 26B that has undergone 'abliteration' — a fine-tuning technique that removes built-in refusal behaviors, making it more willing to respond to prompts that the base model would typically decline. With only ~4B parameters active at inference time despite a 26B total parameter count, it runs efficiently. Trade-off: safety guardrails are intentionally removed.

Capabilities

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

Creative Writing

Strong

Instruction Following

Strong

Factual Knowledge

Use Case Fit

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

Glossary

AbliterationA technique that removes or disables a model's built-in safety refusal mechanisms, allowing it to respond to a wider range of requests.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.Fine-TuningThe process of further training a pre-trained model on new data to adapt it for specific tasks or domains.GuardrailsSafety mechanisms built into a model to refuse harmful requests or prevent it from generating unsafe content.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.Inference TimeThe amount of time it takes for a model to process input and generate output after it has been trained.Parameter CountThe total number of adjustable weights in a model; more parameters generally mean more capacity to learn, but also require more computing power.ParametersThe learned numerical values in a model — more parameters generally means more capacity but higher compute cost.Safety GuardrailsBuilt-in restrictions or filters that prevent a model from generating harmful, illegal, or unethical content.