DeepSeek V4 Flash 0731 Abliterated DS4 Quality128

DeepSeek

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

Released August 2026context N/A

A community-modified variant of DeepSeek V4 Flash that has undergone 'abliteration' — a technique that removes refusal behaviors from the base model. This means it responds without the typical safety filters, making it more permissive but also less guarded. The Q128 quantization suggests a high-fidelity weight compression for local deployment.

Capabilities

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

Instruction Following

Moderate

Creative Writing

Moderate

Multimodal

Use Case Fit

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

DeepSeek V4 Flash 0731 Abliterated DS4 Quality128

DeepSeek

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

Released August 2026context N/A

A community-modified variant of DeepSeek V4 Flash that has undergone 'abliteration' — a technique that removes refusal behaviors from the base model. This means it responds without the typical safety filters, making it more permissive but also less guarded. The Q128 quantization suggests a high-fidelity weight compression for local deployment.

Capabilities

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

Instruction Following

Moderate

Creative Writing

Moderate

Multimodal

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.Community-ModifiedA model that has been altered or fine-tuned by users outside the original development team, often to change its behavior or capabilities.FidelityThe degree to which a quantized or compressed model preserves the quality and accuracy of the original full-precision model.Local DeploymentRunning a model directly on your own computer or server instead of sending requests to a remote service.QuantizationReducing a model's numerical precision (e.g., from 16-bit to 4-bit) to shrink memory usage and speed up inference.Safety FiltersBuilt-in constraints that prevent a model from generating harmful, offensive, or inappropriate content in its responses.