Release of Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled APEX-MTP GGUF

A new GGUF quantization of the Qwen3.6-35B-A3B model, distilled from Claude 4.7 Opus reasoning patterns and optimized via APEX-MTP, has been released for the local LLM community.

Model Overview

The recently released Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled represents a sophisticated intersection of model distillation and efficient quantization. This model leverages reasoning capabilities distilled from Claude 4.7 Opus, integrated into the Qwen3.6 architecture, specifically utilizing a Mixture-of-Experts (MoE) configuration with 35B total parameters and 3B active parameters (A3B).

Technical Implementation: APEX-MTP

The release utilizes the APEX (Adaptive Precision for EXpert M) quantization method in GGUF format. APEX is designed to optimize Mixture-of-Experts models by applying adaptive precision to the expert weights, reducing the memory footprint while attempting to preserve the reasoning capabilities inherited from the distillation process.

Hardware and Deployment

The quantization was developed using an NVIDIA DGX Spark with 122 GB of unified memory. This hardware configuration is sufficient for the 30B-50B parameter class of MoE models. For larger-scale models (200B+), the developer utilizes rented H100, H200, or Blackwell compute clusters to achieve the necessary VRAM for high-fidelity quantization.

Community Availability

This release is part of a broader research initiative providing over 30 free APEX MoE quantizations to the open-source community, enabling researchers and developers to run high-reasoning models on consumer-grade or professional workstation hardware via the GGUF format.

Note: The provided source text was truncated; full technical specifications regarding the "MTP" component of the APEX-MTP quantization were not detailed in the original description.