Ling 3.0 tiny MLX 4bit

Ling

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

Released August 2026131K context≈ 98,304 words

A compact, quantized model optimized for Apple Silicon via the MLX framework. The 4-bit quantization keeps memory footprint small, making it practical for running locally on Mac hardware. Trade-offs typical of heavily quantized tiny models apply — reduced precision compared to full-weight variants.

Capabilities

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

Instruction Following

Moderate

Long Context

Moderate

Creative Writing

Use Case Fit

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

Ling 3.0 tiny MLX 4bit

Ling

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

Released August 2026131K context≈ 98,304 words

A compact, quantized model optimized for Apple Silicon via the MLX framework. The 4-bit quantization keeps memory footprint small, making it practical for running locally on Mac hardware. Trade-offs typical of heavily quantized tiny models apply — reduced precision compared to full-weight variants.

Capabilities

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

Instruction Following

Moderate

Long Context

Moderate

Creative Writing

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.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.