base_model: huihui-ai/Huihui-Xing4.0-29B-A4B-abliterated
base_model_relation: quantized
quantized_by: IsValorum
library_name: gguf
license: apache-2.0
language:

• en
• zh
  pipeline_tag: text-generation
  tags:
• gguf
• llama.cpp
• quantized
• quantization
• apex
• apex-quant
• apex-i-nanoplus
• nanoplus
• abliterated
• uncensored
• moe
• mla
• hyper-connections
• reasoning
• text-generation
• coding
• xing4_0

Quick Navigation Index

1. Architecture & Methodology Overview
2. Empirical Benchmarks & Fidelity Verification
3. Quality Spectrum: APEX-I-NanoPlus vs. Standard Flat Quantizations
4. Model Files & Technical Specifications
5. Surgical Tensor Quantization Map (Audited from GGUF)
6. Hardware Throughput & Offload Benchmarks (RTX 30 / 40 / 50 & RAM Streaming)
7. Inference Quickstart
8. Recommended Generation Parameters
9. CRITICAL: Coding Syntax & Repeat Penalty Advisory
10. Optional Support

Xing4.0-29B-A4B APEX-I-NanoPlus Abliterated GGUF

The Next-Generation Frontier MoE - Extreme 11.7 GB Footprint - Fast System RAM Streaming & Massive Context on 16GB VRAM

[!IMPORTANT]

THE DEFINITIVE SPECIFICATION IN THE 11 to 12 GB CEILING (STREAMLINED ARCHITECTURE)

This APEX-I-NanoPlus release represents the specialized ultra-compact configuration for sparse Mixture-of-Experts quantization within an 11 to 12 GB envelope. It compresses the 40-layer backbone into a compact 11.74 GB (10.94 GiB) file, enabling full offload with large context on consumer 16GB VRAM cards and fast memory bandwidth streaming from system RAM.

[!TIP]

EMPIRICAL BENCHMARK & QUALITY COMPARISON

Quantization Specification	File Size (Disk)	Memory Footprint (RAM/VRAM)	Average BPW	WikiText-2 Perplexity	Delta PPL vs BF16 Base	Quality Tier Equivalent
Unquantized BF16 Base	62.40 GB	58.11 GiB	16.00 BPW	7.3060 +/- 0.18633	Baseline (0.0000)	Full precision reference
APEX-I-MiniPlus V2.1	13.76 GB	12.82 GiB	3.42 BPW	7.7238 +/- 0.19671	+0.4178 (+5.71%)	Q5_K_M tier (bordering Q6_K)
APEX-I-NanoPlus (CURRENT)	11.74 GB	10.94 GiB	2.92 BPW	8.3197 +/- 0.21528	+1.0137 (+13.87%)	Solid Q4_K_M / Q4_K_S Tier

Looking for higher precision? Xing4.0-29B-A4B APEX-I-MiniPlus-V2.1 offers the full 13.76 GB (12.82 GiB / 3.42 BPW) release, delivering Q5_K_M tier (bordering Q6_K) fidelity.

Routing: 100% of recipe-designated ffn_gate_inp router matrices remain in uncompressed F32, guaranteeing zero routing drift.

• Solid Q4_K_M Tier in Language Modeling: WikiText-2 perplexity preserves 4-bit distributional fidelity (8.3197 +/- 0.21528) across standard generation in an ultra-lean footprint.
• Solid Q4_K Tier in MoE Foundation Knowledge: Shared expert tensors (shexp) run in Q4_K, keeping core knowledge intact across 100% of tokens.
• Protected MLA Geometry: Latent attention projections in Q4_K and the output head in Q6_K prevent attention collapse.

[!WARNING]

DO NOT CONFUSE APEX-I-NANOPLUS WITH UNCALIBRATED SUB-3-BIT QUANTS!

Never confuse handcrafted APEX-I-NanoPlus builds with uncalibrated homogeneous quantizations:

• Uncalibrated Homogeneous Quants: Uniformly crush all core MoE experts down to aggressive 2-bit codebooks without importance calibration, leave the sensitive token output head unarmored, and compress latent attention projections down to sub-3-bit. In deep reasoning models with MLA, this triggers severe perplexity spikes, routing misdirection, and broken syntax.
• Handcrafted APEX-I-NanoPlus: Applies a surgical tensor-by-tensor architecture that preserves 100% of expert routing matrices in uncompressed F32 (zero router drift), armors the token output head in high-precision Q6_K, safeguards attention projections in Q4_K, locks shared experts in Q4_K, fortifies down-projection experts in IQ3_XXS, and restricts 2-bit compression strictly to redundant gating/up projections guided by the official imatrix.

[!TIP]

SYSTEM RAM INFERENCE: FULL OR PARTIAL

This APEX-I-NanoPlus release is designed for full or partial system-RAM inference. Depending on the processor, memory bandwidth, and DDR4/DDR5 configuration, generation can range from 20 to 45 tok/s. With partial GPU offload, systems that cannot fit large contexts entirely in VRAM can place the remaining model and context load in system RAM, maintaining stable, responsive generation at longer context lengths.

Architecture & Methodology Overview

The APEX-I-NanoPlus release applies an ultra-compact tensor-by-tensor allocation engineered specifically for sparse Mixture-of-Experts architectures with Multi-head Latent Attention (MLA), prioritizing execution speed and extreme memory efficiency:

• Zero Router Drift (F32): All routing matrices (ffn_gate_inp and ffn_gate_inp_shexp) are preserved in full 32-bit floating point, guaranteeing 100% routing fidelity matching the uncompressed base model.
• Shared Foundation Backbone (Q4_K): The shared expert (shexp) active across 100% of tokens is armored in linear Q4_K across all 40 layers, securing foundational domain knowledge and smooth CPU/GPU streaming.
• MLA Latent Projection Armor (Q4_K / Q6_K): Latent attention projections and output heads are preserved in Q4_K and Q6_K, eliminating attention collapse while keeping memory overhead minimal.
• Fortified Residual Down-Projections (IQ3_XXS): All ffn_down_exps matrices are maintained at 3.06 BPW to protect the residual stream, while redundant SwiGLU gating and up-projections (ffn_gate_exps, ffn_up_exps) are compressed to 2.50 BPW (IQ2_S) using an authentic 820-chunk importance matrix.
• Speculative Head Preservation (blk.40): The Multi-Token Prediction (MTP) draft head is quantized to Q4_K, enabling speculative acceleration in compatible runtimes.

Empirical Benchmarks & Fidelity Verification

Evaluated directly on the compiled GGUF binary using llama-perplexity over the standard WikiText-2 benchmark:

Quality Spectrum: APEX-I-NanoPlus vs. Standard Flat Quantizations

How the handcrafted APEX-I-NanoPlus architecture compares against standard flat quantizations in llama.cpp on 29B Mixture-of-Experts architectures:

Model Files & Technical Specifications

• Base Model: huihui-ai/Huihui-Xing4.0-29B-A4B-abliterated
• Parameters: 29.1B total (approx. 4.0B active per token)
• Architecture: 40 layers, 64 routed MoE experts (4 active per token) + 1 shared expert + Multi-head Latent Attention (MLA)
• Context Length: Native 131,072 tokens (128K)
• Refusal Removal: Orthogonal activation steering via Heretic Directional Ablation

Surgical Tensor Quantization Map (Audited from GGUF)

The exact tensor breakdown below has been verified directly from the compiled binary weights:

Hardware Throughput & Offload Benchmarks (RTX 30 / 40 / 50 & RAM Streaming)

Empirically verified in llama.cpp:

Inference Quickstart

1. llama-cli (Console Generation)

llama-cli \
  -m Xing4.0-29B-A4B.APEX-I-NanoPlus-Abliterated.gguf \
  -ngl 99 \
  -c 16384 \
  --temp 0.7 \
  --top-p 0.90 \
  -p "<|im_start|>system\nYou are an expert uncensored assistant.<|im_end|>\n<|im_start|>user\nWrite a complete Rust implementation of a concurrent ring buffer.<|im_end|>\n<|im_start|>assistant\n"

2. llama-server (OpenAI-Compatible API)

llama-server \
  -m Xing4.0-29B-A4B.APEX-I-NanoPlus-Abliterated.gguf \
  --port 8080 \
  --host 0.0.0.0 \
  -ngl 99 \
  -c 32768 \
  --flash-attn on \
  --cache-type-k q8_0 \
  --cache-type-v q8_0

Recommended Generation Parameters

[!IMPORTANT]

CRITICAL ADVISORY FOR CODING WORKFLOWS: PREVENTING SYNTAX & TOKEN SWAPPING

In programming code, brackets ({, }), assignment operators (=), and indentation whitespace repeat constantly across multi-line structures.

Common Issue: Many local frontends ship with repeat_penalty set to 1.1 or 1.15. Applying repeat penalties to code artificially penalizes necessary syntax tokens. When the logit of { drops, the model is forced to emit the next closest mathematical token, resulting in character swapping or dropped whitespace.

Recommendation: When executing code generation or debugging tasks, always set repeat_penalty to 1.00 or maximum 1.02.

Optional Support

If you find this surgical quantization valuable for your production workflows, local research, or agentic coding environments, consider starring the repository and following the IsValorum Hugging Face profile for continuous updates and frontier releases.