Model Details of Qwopus3.6-35B-A3B-Coder-APEX-GGUF
⚡ Each donation = another big MoE quantized
I host
30+ 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.
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 (first/last 5) get higher precision, middle layers get more aggressive compression. I-variants use diverse imatrix calibration (chat, code, reasoning, tool-calling, agentic traces, Wikipedia).
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Qwopus3.6-35B-A3B-Coder-APEX-GGUF is an open source model from GitHub that offers a free installation service, and any user can find Qwopus3.6-35B-A3B-Coder-APEX-GGUF on GitHub to install. At the same time, huggingface.co provides the effect of Qwopus3.6-35B-A3B-Coder-APEX-GGUF install, users can directly use Qwopus3.6-35B-A3B-Coder-APEX-GGUF installed effect in huggingface.co for debugging and trial. It also supports api for free installation.
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