Qwopus 3.6 35B-A3B v1 APEX GGUF
APEX (Adaptive Precision for EXpert Models) quantizations of Jackrong/Qwopus3.6-35B-A3B-v1.
Brought to you by the LocalAI team | APEX Project
Available Files
| File | Profile | Size | Best For |
|---|---|---|---|
| Qwopus3.6-35B-A3B-v1-APEX-I-Quality.gguf | I-Quality | 23 GB | Highest quality with imatrix |
| Qwopus3.6-35B-A3B-v1-APEX-Quality.gguf | Quality | 23 GB | Highest quality standard |
| Qwopus3.6-35B-A3B-v1-APEX-I-Balanced.gguf | I-Balanced | 25 GB | Best overall quality/size ratio |
| Qwopus3.6-35B-A3B-v1-APEX-Balanced.gguf | Balanced | 25 GB | General purpose |
| Qwopus3.6-35B-A3B-v1-APEX-I-Compact.gguf | I-Compact | 17 GB | Consumer GPUs, best quality/size |
| Qwopus3.6-35B-A3B-v1-APEX-Compact.gguf | Compact | 17 GB | Consumer GPUs |
| Qwopus3.6-35B-A3B-v1-APEX-I-Mini.gguf | I-Mini | 14 GB | Smallest viable, fastest inference |
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).
See the APEX project for full details.
Architecture
- Base Model: Jackrong/Qwopus3.6-35B-A3B-v1
- Architecture: Qwen3.5-MoE 35B-A3B
- Layers: 40
- Experts: 256 routed (8 active per token)
- Total Parameters: ~35B
- Active Parameters: ~3B per token
- APEX Config: 6+6 symmetric edge gradient across 40 layers
- Calibration: v1.3 diverse dataset (chat, code, reasoning, tool-calling, multilingual)
Run with LocalAI
local-ai run mudler/Qwopus3.6-35B-A3B-v1-APEX-GGUF@Qwopus3.6-35B-A3B-v1-APEX-I-Balanced.gguf
Credits
APEX is brought to you by the LocalAI team. Developed through human-driven, AI-assisted research. Built on llama.cpp.