oceanicity/Qwen3-4B-Instruct-CoreML-8bit

Qwen3-4B-Instruct - CoreML (8-Bit Quantised)

This is an 8-bit quantised CoreML conversion of the oceanicity/Qwen3-4B-Instruct-2507 model. It has been heavily optimised for fast, efficient, and low-memory inference on Apple Silicon using the Apple Neural Engine (ANE).

Conversion and quantisation work was performed using a customised version of 0seba's coremlmodels tool.

Model Details

• Architecture: Qwen3 (4 Billion Parameters)
• Precision: 8-bit Weights (Linear Symmetric, Per-Channel) / 16-bit Activations
• Context Length: 8,192 Tokens (KV Cache)
• Format: CoreML .mlpackage chunks

Optimisations Applied

• Linear-to-Conv2d Patching: Transformer linear layers were patched into 1x1 convolutions to better align with the Neural Engine backend.
• RMSNorm Fusion: Layer normalisation layers were fused using CoreMLTools graph passes to prevent FP16 overflow.
• Chunking: The model was split into 8 chunks to safely bypass the Neural Engine's hardware memory limits per segment.
• Vocabulary Chunking: The massive LM head was exported as a standalone chunked model to bypass the ~16,384 dimension limit on Apple Silicon.
• Pre-computed Position Embeddings: RoPE embeddings were computed statically during tracing to avoid precision loss and runtime math overhead.

Files Included

• chunk_0.mlpackage through chunk_7.mlpackage: The core transformer layers.
• lm_head.mlpackage: The chunked vocabulary output head.
• embeddings.npy: The standalone token embedding weights.

Usage

This model is ready to be used in CoreML inference pipelines that support multi-chunked stateful transformers. Ensure that your inference engine stitches the chunks together sequentially and routes the KV cache states appropriately.