Model Overview

This implementation ports Switf-1.5 Qwen3.8 27B with GSQ-RCO quantization into Ollama-compatible GGUF format. The architecture retains complete fidelity to the original model while enabling efficient inference through mixed-precision quantization.

Key Architecture Features:

• Based on Qwen3.8 27B with Swift-specific post-training focused on long-horizon, agentic and coding tasks
• Uses 58.5% fewer thinking tokens than base model
• Demonstrates 9.18× speed-up on several coding and reasoning tasks compared to base Qwen

Available Quantization Tiers (Q3 Variants Only)

Performance Characteristics from Swift-1.5

The model demonstrates significant efficiency gains over the base Qwen architecture:

• 0.35% accuracy improvement over base model while using significantly fewer tokens
• KLD (Kullback-Leibler divergence) measurements confirm improved fidelity compared to standard quantizations across multiple domains including prose, code, math text, and multilingual capabilities

Quantization Details from Source

The implementation reuses ISTA-DASLab’s GSQ-RCO per-tensor allocation with Swift-specific importance matrix (V1MIX) for quantization. The Q3 variants utilize preserved V1MIX variants of the allocation profiles.