---

license: apache-2.0

base_model: lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled

tags:

- gguf

- quantized

- apex

- moe

- mixture-of-experts

- qwen3

- vlm

- vision

- reasoning

- distilled

- claude-opus

---

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Qwen3.6 35B-A3B — Claude 4.7 Opus Reasoning Distilled — APEX GGUF

APEX (Adaptive Precision for EXpert Models) quantizations of lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled.

Brought to you by the LocalAI team | APEX Project | Technical Report

Available Files

| File | Profile | Size | Best For |

|------|---------|------|----------|

| Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-I-Balanced.gguf | I-Balanced | 24 GB | Best overall quality/size ratio |

| Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-Balanced.gguf | Balanced | 24 GB | General purpose |

| Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-I-Quality.gguf | I-Quality | 21 GB | Highest quality with imatrix |

| Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-Quality.gguf | Quality | 21 GB | Highest quality standard |

| Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-I-Compact.gguf | I-Compact | 16 GB | Consumer GPUs, best quality/size |

| Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-Compact.gguf | Compact | 16 GB | Consumer GPUs |

| Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-I-Mini.gguf | I-Mini | 13 GB | Smallest "safe" tier |

| Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-I-Nano.gguf | I-Nano | 11 GB | Experimental — IQ2_XXS mid-layer experts |

| Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-F16.gguf | F16 reference | 65 GB | Full-precision reference |

| mmproj.gguf | Vision projector | ~1 GB | Required for image understanding |

What is APEX?

APEX is a quantization strategy for Mixture-of-Experts (MoE) models. It classifies tensors by role (routed expert, shared expert, attention) and applies a layer-wise precision gradient — edge layers get higher precision, middle layers get more aggressive compression. I-variants use diverse imatrix calibration (chat, code, reasoning, tool-calling, agentic traces, Wikipedia).

The key insight: in MoE models, expert FFN tensors make up the bulk of model weight but only ~8/256 experts activate per token. APEX compresses middle-layer experts more aggressively while preserving edge layers (first/last 5) and keeping attention, SSM/Mamba, and shared expert tensors at higher precision.

See the APEX project for full details, technical report, and scripts.

Nano (experimental tier)

The APEX Nano tier pushes mid-layer routed experts to IQ2_XXS (2.06 bpw), near-edge to IQ2_S, edges to Q3_K, with shared experts kept at Q5_K. About 20% smaller than Mini with modest quality cost — viable only on MoE thanks to sparse per-token expert activation. Requires imatrix.

Benchmarks pending. Feedback welcome.

Architecture

• Model: Qwen3.6 35B-A3B Claude 4.7 Opus Reasoning Distilled
• Base: Qwen 3.6 35B-A3B
• Layers: 40
• Experts: 256 routed + shared (8 active per token)
• Total Parameters: ~35B
• Active Parameters: ~3B per token
• Attention: Hybrid (full attention every 4th layer, linear/Mamba otherwise)
• Vision: Built-in vision encoder (mmproj included)
• APEX Config: 5+5 symmetric edge gradient across 40 layers
• Calibration: v1.3 diverse dataset (chat, code, reasoning, multilingual, tool-calling, Wikipedia)

Run with LocalAI

local-ai run mudler/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-GGUF@Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-I-Balanced.gguf

Credits

• Reasoning distill fine-tune: lordx64
• Vision projector (mmproj): mradermacher
• APEX quantization: LocalAI team
• Built on llama.cpp