mudler/Qwen3.6-35B-A3B-uncensored-heretic-APEX-GGUF
mudler • general⚡ Each donation = another big MoE quantized
I host 25+ free APEX MoE quantizations as independent research. My only local hardware is an NVIDIA DGX Spark (122 GB unified memory) — enough for ~30-50B-class MoEs, but bigger ones (200B+) require rented compute on H100/H200/Blackwell, typically $20-100 per quant.
If APEX quants are useful to you, your support directly funds those bigger runs.
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💚 Big thanks to Hugging Face for generously donating additional storage — much appreciated.
Qwen3.6 35B-A3B Uncensored Heretic APEX GGUF
APEX (Adaptive Precision for EXpert Models) quantizations of llmfan46/Qwen3.6-35B-A3B-uncensored-heretic.
Brought to you by the LocalAI team | APEX Project | Technical Report
Available Files
| File | Profile | Size | Best For |
|---|---|---|---|
| Qwen3.6-35B-A3B-uncensored-heretic-APEX-I-Balanced.gguf | I-Balanced | 24 GB | Best overall quality/size ratio |
| Qwen3.6-35B-A3B-uncensored-heretic-APEX-Balanced.gguf | Balanced | 24 GB | General purpose |
| Qwen3.6-35B-A3B-uncensored-heretic-APEX-I-Quality.gguf | I-Quality | 22 GB | Highest quality with imatrix |
| Qwen3.6-35B-A3B-uncensored-heretic-APEX-Quality.gguf | Quality | 22 GB | Highest quality standard |
| Qwen3.6-35B-A3B-uncensored-heretic-APEX-I-Compact.gguf | I-Compact | 17 GB | Consumer GPUs, best quality/size |
| Qwen3.6-35B-A3B-uncensored-heretic-APEX-Compact.gguf | Compact | 17 GB | Consumer GPUs |
| Qwen3.6-35B-A3B-uncensored-heretic-APEX-I-Mini.gguf | I-Mini | 14 GB | Smallest viable, fastest inference |
| 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.
Architecture
- Model: Qwen3.6 35B-A3B Uncensored Heretic (uncensored fine-tune)
- 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-uncensored-heretic-APEX-GGUF@Qwen3.6-35B-A3B-uncensored-heretic-APEX-I-Balanced.gguf