Huihui Qwen3.6 35B A3B abliterated FP8 DYNAMIC

Huihui

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

Released April 2026context N/A35B params

A large multimodal model that accepts both text and image inputs, derived from the Qwen3 architecture at 35B parameters with a 3B active parameter mixture-of-experts configuration. The 'abliterated' tag indicates refusal behaviors have been removed, meaning it responds more freely to a wider range of prompts without built-in content restrictions. Running in FP8 dynamic quantization keeps memory footprint manageable for its size.

Capabilities

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

Long Context

Moderate

Tool Use

Huihui Qwen3.6 35B A3B abliterated FP8 DYNAMIC

Huihui

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

Released April 2026context N/A35B params

A large multimodal model that accepts both text and image inputs, derived from the Qwen3 architecture at 35B parameters with a 3B active parameter mixture-of-experts configuration. The 'abliterated' tag indicates refusal behaviors have been removed, meaning it responds more freely to a wider range of prompts without built-in content restrictions. Running in FP8 dynamic quantization keeps memory footprint manageable for its size.

Capabilities

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

Long Context

Moderate

Tool Use

Glossary

AbliteratedA model variant where safety filters and refusal mechanisms have been removed, allowing it to respond to requests without built-in content restrictions.ArchitectureThe underlying structural design of a neural network that defines how data flows through layers and components.Content RestrictionsSafety guidelines and filters built into a model to prevent it from generating harmful, illegal, or unethical content.Dynamic QuantizationA quantization approach that adjusts precision levels during inference based on the input data, optimizing the balance between speed and accuracy on-the-fly.FP8 Dynamic QuantizationA compression technique that reduces model size and speeds up inference by representing weights and activations using 8-bit floating-point numbers, with dynamic scaling adjusted per batch to maintain accuracy.FP8 Dynamic QuantizationA specific quantization method that uses 8-bit floating-point numbers and adjusts precision dynamically based on the data being processed, balancing speed and accuracy.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.MultimodalA model that can process and understand multiple types of input, such as both text and images.Multimodal ModelAn AI model that can process and understand multiple types of input data, such as video, images, and text together.ParametersThe learned numerical values in a model — more parameters generally means more capacity but higher compute cost.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