Ornith 1.5 35B A3B MLX 6bit

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

Released August 2026context N/A

Ornith 1.5 35B A3B MLX 6bit is a quantized, open-weight text model optimized for Apple Silicon via the MLX framework, running at 6-bit precision. The A3B designation suggests a sparse or mixture-of-experts-style architecture where only a subset of parameters are active per inference, balancing a large 35B parameter count against practical memory constraints. Details about its specific reasoning or language capabilities beyond text generation are limited from available data.

Capabilities

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

Reasoning & Logic

Moderate

Long Context

Moderate

Instruction Following

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 6bit

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

Released August 2026context N/A

Ornith 1.5 35B A3B MLX 6bit is a quantized, open-weight text model optimized for Apple Silicon via the MLX framework, running at 6-bit precision. The A3B designation suggests a sparse or mixture-of-experts-style architecture where only a subset of parameters are active per inference, balancing a large 35B parameter count against practical memory constraints. Details about its specific reasoning or language capabilities beyond text generation are limited from available data.

Capabilities

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

Reasoning & Logic

Moderate

Long Context

Moderate

Instruction Following

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.ArchitectureThe underlying structural design of a neural network that defines how data flows through layers and components.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.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.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.Parameter CountThe total number of adjustable weights in a model; more parameters generally mean more capacity to learn, but also require more computing power.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.QuantizedA technique that reduces a model's size and memory usage by storing weights with lower precision (fewer bits), trading some accuracy for efficiency.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.Text GenerationThe process of an AI model creating new text one word or token at a time based on patterns it learned during training.Text ModelA language model that processes and generates only text, without support for images, audio, or other media types.