Ornith 1.5 35B A3B MLX 4bit

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

Released August 2026context N/A

Ornith 1.5 35B A3B MLX 4bit is a quantized, open-weight text model optimized for Apple Silicon via the MLX framework. The 4-bit quantization means it trades some precision for dramatically reduced memory footprint, making a 35B-parameter model runnable on consumer hardware. Details about its specific reasoning or language capabilities beyond text generation are limited.

Capabilities

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

Long Context

Moderate

Coding

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 4bit

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

Released August 2026context N/A

Ornith 1.5 35B A3B MLX 4bit is a quantized, open-weight text model optimized for Apple Silicon via the MLX framework. The 4-bit quantization means it trades some precision for dramatically reduced memory footprint, making a 35B-parameter model runnable on consumer hardware. Details about its specific reasoning or language capabilities beyond text generation are limited.

Capabilities

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

Long Context

Moderate

Coding

Moderate

Factual Knowledge

Use Case Fit

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

Glossary

4-bit QuantizationA specific type of quantization that represents model weights using only 4 bits instead of the original 32 bits, enabling very efficient inference on consumer hardware.Apple SiliconApple's custom-designed processors (like M1, M2, M3) optimized for running machine learning models on Mac computers.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.Parameter ModelA neural network described by the number of learnable weights it contains; more parameters generally mean greater capacity to learn complex patterns, but also require more computational resources.PrecisionThe level of numerical detail a model uses to represent its internal values; higher precision means more accurate calculations but requires more memory.QuantizationReducing a model's numerical precision (e.g., from 16-bit to 4-bit) to shrink memory usage and speed up inference.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.