mudler/Qwopus3.6-35B-A3B-Coder-APEX-MTP-GGUF overview
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Runs locally from ~861.0 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| Qwopus3.6-35B-A3B-Coder-APEX-MTP-Balanced.gguf | GGUF | GGUF | 24.27 GB | Download |
| Qwopus3.6-35B-A3B-Coder-APEX-MTP-Compact.gguf | GGUF | GGUF | 16.14 GB | Download |
| Qwopus3.6-35B-A3B-Coder-APEX-MTP-I-Balanced.gguf | GGUF | GGUF | 24.27 GB | Download |
| Qwopus3.6-35B-A3B-Coder-APEX-MTP-I-Compact.gguf | GGUF | GGUF | 16.14 GB | Download |
| Qwopus3.6-35B-A3B-Coder-APEX-MTP-I-Mini.gguf | GGUF | GGUF | 13.29 GB | Download |
| Qwopus3.6-35B-A3B-Coder-APEX-MTP-I-Quality.gguf | GGUF | GGUF | 21.87 GB | Download |
| Qwopus3.6-35B-A3B-Coder-APEX-MTP-Quality.gguf | GGUF | GGUF | 21.87 GB | Download |
| mmproj.gguf | GGUF | GGUF | 861.0 MB | Download |
Model Details
Model README
---
license: apache-2.0
base_model: Jackrong/Qwopus3.6-35B-A3B-Coder
tags:
- gguf
- quantized
- apex
- apex-mtp
- moe
- mixture-of-experts
- qwen3
- speculative-decoding
- self-speculative
- mtp
- vlm
- vision
- coder
---
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<h2 style="color: white; margin: 0 0 10px 0;">⚡ Each donation = another big MoE quantized</h2>
<p style="font-size: 18px; margin: 0 0 15px 0;">I host <b>30+ free APEX MoE quantizations</b> as independent research. My only local hardware is an <b>NVIDIA DGX Spark</b> (122 GB unified memory) — enough for ~30-50B-class MoEs, but <b>bigger ones (200B+) require rented compute</b> on H100/H200/Blackwell, typically $20-100 per quant.<br>If APEX quants are useful to you, your support directly funds those bigger runs.</p>
<p style="font-size: 20px; margin: 0;">
<a href="https://www.patreon.com/cw/mudler" style="color: white; text-decoration: underline;">🎉 Patreon (Monthly)</a> |
<a href="https://www.buymeacoffee.com/mudler" style="color: white; text-decoration: underline;">☕ Buy Me a Coffee</a> |
<a href="https://github.com/sponsors/mudler" style="color: white; text-decoration: underline;">⭐ GitHub Sponsors</a>
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Qwopus3.6-35B-A3B-Coder — APEX-MTP GGUF
APEX (Adaptive Precision for EXpert Models) quantizations of Jackrong/Qwopus3.6-35B-A3B-Coder, with the model's MTP (multi-token prediction) head bundled for in-the-box self-speculative decoding.
Brought to you by the LocalAI team | APEX Project | Technical Report
What's different from the plain APEX repo?
This model ships a real MTP head, and these GGUFs bundle it alongside the trunk in a single file (via llama.cpp PR #22673). With a recent llama.cpp you can enable self-speculative decoding from just this one file — no separate draft model:
llama-server -m Qwopus3.6-35B-A3B-Coder-APEX-MTP-I-Balanced.gguf --draft-mtp
The non-MTP version is at mudler/Qwopus3.6-35B-A3B-Coder-APEX-GGUF — slightly smaller, no self-spec.
MTP draft head precision
The bundled MTP head (blk.40. including nextn.) is quantized to Q8_0 (near-lossless) on every tier, keeping draft accuracy high for a good spec-decode acceptance rate at a modest size cost. The MTP head is not imatrix-calibrated (imatrix forward passes only activate the trunk), so it uses static Q8_0.
Available Files
| File | Profile | Best For |
|------|---------|----------|
| Qwopus3.6-35B-A3B-Coder-APEX-MTP-I-Balanced.gguf | I-Balanced | Best overall + self-spec |
| Qwopus3.6-35B-A3B-Coder-APEX-MTP-I-Quality.gguf | I-Quality | Highest quality with imatrix + self-spec |
| Qwopus3.6-35B-A3B-Coder-APEX-MTP-Quality.gguf | Quality | Highest quality (no imatrix) |
| Qwopus3.6-35B-A3B-Coder-APEX-MTP-Balanced.gguf | Balanced | General purpose |
| Qwopus3.6-35B-A3B-Coder-APEX-MTP-I-Compact.gguf | I-Compact | Consumer GPUs + self-spec |
| Qwopus3.6-35B-A3B-Coder-APEX-MTP-Compact.gguf | Compact | Consumer GPUs |
| Qwopus3.6-35B-A3B-Coder-APEX-MTP-I-Mini.gguf | I-Mini | Smallest viable + self-spec |
| mmproj.gguf | Vision projector | Required for image understanding |
Architecture
- Base: Qwopus3.6-35B-A3B-Coder (Qwen3_5MoeForConditionalGeneration, Qwen3.6-35B-A3B)
- Layers: 40 trunk + 1 MTP (bundled) · Experts: 256 routed + 1 shared (8 active)
- Vision: Built-in vision encoder (mmproj included)
- Calibration: v1.3 diverse dataset
Credits
APEX by the LocalAI team. MTP support: llama.cpp PR #22673. Built on llama.cpp. Base model by Jackrong.
Run mudler/Qwopus3.6-35B-A3B-Coder-APEX-MTP-GGUF with guIDE
Download guIDE — the AI-native code editor with local LLM inference and 69 built-in tools.
Source: Hugging Face · Compare models