license: apache-2.0
base_model: LuffyTheFox/Qwen3.5-35B-A3B-Uncensored-FernflowerAI-safetensors
tags:
- gguf
- quantized
- apex
- moe
- mixture-of-experts
- qwen3
- qwen3.5
- uncensored
- hybrid
⚡ 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.
🎉 Patreon (Monthly) | ☕ Buy Me a Coffee | ⭐ GitHub Sponsors
💚 Big thanks to Hugging Face for generously donating additional storage, much appreciated.
Qwen3.5-35B-A3B-Uncensored-FernflowerAI — APEX GGUF
APEX (Adaptive Precision for EXpert Models) quantizations of LuffyTheFox/Qwen3.5-35B-A3B-Uncensored-FernflowerAI-safetensors.
Brought to you by the LocalAI team | APEX Project | Technical Report
Available Files
| File | Profile | Size | Best For |
|---|---|---|---|
| Qwen3.5-35B-A3B-Uncensored-FernflowerAI-APEX-I-Balanced.gguf | I-Balanced | 26 GB | Best overall quality/size ratio |
| Qwen3.5-35B-A3B-Uncensored-FernflowerAI-APEX-Balanced.gguf | Balanced | 26 GB | General purpose |
| Qwen3.5-35B-A3B-Uncensored-FernflowerAI-APEX-I-Quality.gguf | I-Quality | 22 GB | Highest quality with imatrix |
| Qwen3.5-35B-A3B-Uncensored-FernflowerAI-APEX-Quality.gguf | Quality | 22 GB | Highest quality standard |
| Qwen3.5-35B-A3B-Uncensored-FernflowerAI-APEX-I-Compact.gguf | I-Compact | 17 GB | Consumer GPUs, best quality/size |
| Qwen3.5-35B-A3B-Uncensored-FernflowerAI-APEX-Compact.gguf | Compact | 17 GB | Consumer GPUs |
| Qwen3.5-35B-A3B-Uncensored-FernflowerAI-APEX-I-Mini.gguf | I-Mini | 14 GB | Smallest "safe" tier |
| Qwen3.5-35B-A3B-Uncensored-FernflowerAI-APEX-I-Nano.gguf | I-Nano | 11 GB | Experimental — IQ2_XXS mid-layer experts |
| Qwen3.5-35B-A3B-Uncensored-FernflowerAI-F16.gguf | F16 reference | 65 GB | Full-precision reference (text-only) |
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
- Base: Qwen 3.5 35B-A3B (Qwen3_5MoeForConditionalGeneration / Qwen3_5MoeForCausalLM)
- Layers: 40
- Experts: 256 routed + 1 shared (8 active per token)
- Total Parameters: ~35B
- Active Parameters: ~3B per token
- Hidden size: 2048
- Attention: Hybrid (full attention every 4th layer, linear/Mamba otherwise)
- Vision: Vision config present in upstream model but no mmproj uploaded by author — text-only inference for now
- 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/Qwen3.5-35B-A3B-Uncensored-FernflowerAI-APEX-GGUF@Qwen3.5-35B-A3B-Uncensored-FernflowerAI-APEX-I-Balanced.gguf
Credits
- Uncensored fine-tune: LuffyTheFox
- Base: HauhauCS-Aggressive
- APEX quantization: LocalAI team
- Built on llama.cpp