Ornith 1.5 35B A3B MLX 8bit

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

Released August 2026context N/A

Ornith 1.5 35B A3B MLX 8bit is an open-weight text model optimized for Apple Silicon via the MLX framework, quantized to 8-bit for reduced memory footprint. The A3B designation likely refers to an architectural or mixture-of-experts configuration, though specific capability details from ornith-ai are limited. It trades some precision for accessibility, making it runnable on local hardware where full-precision large models would struggle.

Capabilities

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

Long Context

Moderate

Creative Writing

Moderate

Factual Knowledge

Use Case Fit

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

Ornith 1.5 35B A3B MLX 8bit

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

Released August 2026context N/A

Ornith 1.5 35B A3B MLX 8bit is an open-weight text model optimized for Apple Silicon via the MLX framework, quantized to 8-bit for reduced memory footprint. The A3B designation likely refers to an architectural or mixture-of-experts configuration, though specific capability details from ornith-ai are limited. It trades some precision for accessibility, making it runnable on local hardware where full-precision large models would struggle.

Capabilities

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

Long Context

Moderate

Creative Writing

Moderate

Factual Knowledge

Use Case Fit

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

Glossary

AccessibilityDesigning technology so people with disabilities can use it effectively.Apple SiliconApple's custom-designed processors (like M1, M2, M3) optimized for running machine learning models on Mac computers.Full-PrecisionA model using standard 32-bit floating-point numbers to represent weights, providing maximum accuracy but requiring more memory.MLXA machine learning framework optimized for running models efficiently on Apple Silicon chips.MLX FrameworkA machine learning framework specifically designed for running AI models efficiently on Apple Silicon hardware.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.PrecisionThe level of numerical detail a model uses to represent its internal values; higher precision means more accurate calculations but requires more memory.QuantizedA technique that reduces a model's size and memory usage by storing weights with lower precision (fewer bits), trading some accuracy for efficiency.Text ModelA language model that processes and generates only text, without support for images, audio, or other media types.