Huihui gpt oss 20b mxfp4 abliterated v2

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

Released September 2025131K context≈ 98,304 words

A 20-billion parameter open-weight model from huihui-ai that has undergone 'abliteration' — a technique that removes refusal behaviors, making it respond more openly to a wider range of prompts without built-in content restrictions. The MXFP4 quantization keeps memory footprint manageable while preserving much of the original model's capability. This is a modified derivative, so behavior can differ noticeably from its base model.

Capabilities

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

Long Context

Strong

Creative Writing

Huihui gpt oss 20b mxfp4 abliterated v2

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

Released September 2025131K context≈ 98,304 words

A 20-billion parameter open-weight model from huihui-ai that has undergone 'abliteration' — a technique that removes refusal behaviors, making it respond more openly to a wider range of prompts without built-in content restrictions. The MXFP4 quantization keeps memory footprint manageable while preserving much of the original model's capability. This is a modified derivative, so behavior can differ noticeably from its base model.

Capabilities

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

Long Context

Strong

Creative Writing

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.Content RestrictionsSafety guidelines and filters built into a model to prevent it from generating harmful, illegal, or unethical content.MXFP4A low-precision floating-point format (4-bit) designed for efficient neural network computation while maintaining reasonable accuracy.MXFP4 QuantizationA low-precision numerical format that represents model weights and activations using only 4 bits, significantly reducing memory usage and inference speed at the cost of some accuracy.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.Open-Weight ModelA model whose trained weights are publicly released, allowing anyone to download and run it locally.QuantizationReducing a model's numerical precision (e.g., from 16-bit to 4-bit) to shrink memory usage and speed up inference.

Capabilities

Capabilities

Use Case Fit

Glossary