Ornith 1.5 9B MLX 6bit

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

Released August 2026context N/A

A compact open-weight text model quantized to 6-bit precision for the MLX framework, making it suited for running locally on Apple Silicon hardware. The 9B parameter size places it in a mid-range category where it balances memory footprint against capability. Details about its specific strengths or behavioral tendencies are limited beyond its format and deployment target.

Capabilities

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

Long Context

Moderate

Coding

Moderate

Creative Writing

Use Case Fit

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

Ornith 1.5 9B MLX 6bit

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

Released August 2026context N/A

A compact open-weight text model quantized to 6-bit precision for the MLX framework, making it suited for running locally on Apple Silicon hardware. The 9B parameter size places it in a mid-range category where it balances memory footprint against capability. Details about its specific strengths or behavioral tendencies are limited beyond its format and deployment target.

Capabilities

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

Long Context

Moderate

Coding

Moderate

Creative Writing

Use Case Fit

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

Glossary

6-bit PrecisionA quantization method that represents model weights using only 6 bits per value, significantly reducing memory requirements compared to standard 32-bit floating-point storage.Apple SiliconApple's custom-designed processors (like M1, M2, M3) optimized for running machine learning models on Mac computers.Bit PrecisionThe number of bits used to represent each number in a model; lower bit precision (like 3-bit) means smaller file size but potentially less accurate calculations.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.