---
license: apache-2.0
base_model: llmfan46/Qwen3.6-35B-A3B-uncensored-heretic
tags:
  - gguf
  - quantized
  - apex
  - moe
  - mixture-of-experts
  - qwen3
  - vlm
  - vision
  - uncensored
  - heretic
---





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# Qwen3.6 35B-A3B Uncensored Heretic APEX GGUF

**APEX (Adaptive Precision for EXpert Models)** quantizations of [llmfan46/Qwen3.6-35B-A3B-uncensored-heretic](https://huggingface.co/llmfan46/Qwen3.6-35B-A3B-uncensored-heretic).

**Brought to you by the [LocalAI](https://github.com/mudler/LocalAI) team** | [APEX Project](https://github.com/mudler/apex-quant) | [Technical Report](https://github.com/mudler/apex-quant/blob/main/paper/APEX_Technical_Report.pdf)

## 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](https://github.com/mudler/apex-quant) 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

```bash
local-ai run mudler/Qwen3.6-35B-A3B-uncensored-heretic-APEX-GGUF@Qwen3.6-35B-A3B-uncensored-heretic-APEX-I-Balanced.gguf
```

## Credits

- **Heretic uncensored fine-tune**: [llmfan46](https://huggingface.co/llmfan46)
- **APEX quantization**: [LocalAI](https://github.com/mudler/LocalAI) team
- Built on [llama.cpp](https://github.com/ggerganov/llama.cpp)