Qwen3 235B A22B abliterated FP8

Qwen3

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

Released February 202641K context≈ 30,720 words

A large mixture-of-experts model with 235 billion total parameters but only 22 billion active at a time, keeping inference costs manageable. The 'abliterated' variant has had refusal behaviors removed, meaning it responds to a wider range of prompts without declining. Runs in FP8 precision, trading a small amount of numerical fidelity for reduced memory footprint.

Capabilities

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

Coding

Exceptional

Reasoning & Logic

Qwen3 235B A22B abliterated FP8

Qwen3

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

Released February 202641K context≈ 30,720 words

A large mixture-of-experts model with 235 billion total parameters but only 22 billion active at a time, keeping inference costs manageable. The 'abliterated' variant has had refusal behaviors removed, meaning it responds to a wider range of prompts without declining. Runs in FP8 precision, trading a small amount of numerical fidelity for reduced memory footprint.

Capabilities

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

Coding

Exceptional

Reasoning & Logic

Glossary

AbliteratedA model variant where safety filters and refusal mechanisms have been removed, allowing it to respond to requests without built-in content restrictions.FP8 PrecisionA data format that stores numbers using 8 bits instead of the standard 32 bits, significantly reducing memory requirements with minimal quality loss.FidelityThe degree to which a quantized or compressed model preserves the quality and accuracy of the original full-precision model.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.Numerical FidelityThe accuracy and precision with which a model preserves mathematical calculations; lower fidelity means some precision is lost, often as a trade-off for smaller model size.ParametersThe learned numerical values in a model — more parameters generally means more capacity but higher compute cost.PrecisionThe level of numerical detail a model uses to represent its internal values; higher precision means more accurate calculations but requires more memory.

Capabilities

Capabilities

Use Case Fit

Glossary