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-miniplus
• v2.1
• 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-MiniPlus V2.1 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. The 16GB / 24GB Architecture: Full Context Runs In VRAM
8. Recommended Configuration & Setup
9. Recommended Generation Parameters
10. CRITICAL: Coding Syntax & Repeat Penalty Advisory
11. Optional Support

Xing4.0-29B-A4B APEX-I-MiniPlus-V2.1 Abliterated GGUF

The Next-Generation Frontier MoE - Uncensored & Unconstrained - MLA + Hyper-Connections - Efficient System RAM Offload

[!IMPORTANT]

THE DEFINITIVE SPECIFICATION IN THE 13 to 14 GB CEILING

This APEX-I-MiniPlus-V2.1 release represents the specialized tensor-by-tensor configuration for sparse Mixture-of-Experts quantization within a 13 to 14 GB envelope. Every tensor across its 40 layers, 64 routed experts (4 active per token), dedicated shared expert, and integrated MTP speculative block has been mathematically allocated to maximize reasoning precision, preserve routing behavior, and prevent avoidable CPU dequantization stalls.

[!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 (CURRENT)	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	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

Prefer the ultra-compact Xing edition? APEX-I-NanoPlus compresses the same Xing family down to 11.74 GB (10.94 GiB / 2.92 BPW) for extreme 16GB VRAM and RAM streaming at Solid Q4_K_M tier quality, while MiniPlus V2.1 retains the higher 13.76 GB (12.82 GiB / 3.42 BPW) Q5_K_M tier fidelity.

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

• Q6_K-Bordering Tier in Reasoning & Logic: WikiText-2 perplexity delta is exceptionally low (+0.4178 / +5.71% vs. BF16), placing overall semantic representation at the boundary of a 26 GB Q6_K build within an agile approx. 13.76 GB footprint.
• Q5_K Tier in MoE Foundation Knowledge: All shared expert tensors (shexp) across all 40 layers run in uncompressed Q5_K, keeping core knowledge intact across 100% of tokens.
• Solid MLA & Output Armor: Latent attention projections in Q4_K/Q5_K and the output head in Q6_K prevent token drift, attention collapse, and formatting errors.

[!WARNING]

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

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

• Uncalibrated Homogeneous Quants: Uniformly compress 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-MiniPlus V2.1: Applies a custom tensor-by-tensor architecture that preserves specified router gates in uncompressed F32, armors the token output head in high-precision Q6_K, safeguards attention projections in Q4_K/Q5_K, locks shared experts in Q5_K, and calibrates core reasoning experts with an authentic 820-chunk importance matrix (imatrix).

[!TIP]

SYSTEM RAM INFERENCE: FULL OR PARTIAL

This APEX-I-MiniPlus 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-MiniPlus V2.1 release applies a mathematically audited, tensor-by-tensor allocation engineered specifically for sparse Mixture-of-Experts architectures with Multi-head Latent Attention (MLA):

• Zero Router Drift (F32): All 64 routing matrices (ffn_gate_inp and ffn_gate_inp_shexp) are retained in full 32-bit floating point, ensuring exact expert selection identical to the uncompressed base model.
• Shared Foundation Backbone (Q5_K): The shared expert (shexp) active across 100% of tokens is armored in high-precision linear Q5_K across all 40 layers, securing domain knowledge and syntactic consistency.
• MLA Latent Projection Armor (Q4_K / Q5_K): Latent attention projections (attn_q_a, attn_q_b, attn_v_b, attn_kv_a_mqa) and output heads (attn_output in Q6_K) are safeguarded against the geometric distortion that breaks standard sub-4-bit quantizations.
• Asymmetric MoE Allocation: Boundary layers (0 to 9 and 30 to 39) utilize Q3_K for representation stability, while deep core layers (10 to 29) apply IQ3_XXS calibrated against an authentic 820-chunk importance matrix (imatrix).
• Speculative Head Preservation (blk.40): The Multi-Token Prediction (MTP) draft head is surgically isolated and quantized to Q4_K, ensuring operational speculative decoding without imatrix lookup issues.

Empirical Benchmarks & Fidelity Verification

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

Quality Spectrum: APEX-I-MiniPlus V2.1 vs. Standard Flat Quantizations

How the handcrafted APEX-I-MiniPlus V2.1 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:

The 16GB / 24GB Architecture: Full Context Runs In VRAM

Xing4.0-29B-A4B APEX-I-MiniPlus-V2.1 fits effortlessly within consumer VRAM limits:

Recommended Configuration & Setup

1. llama-cli (Console Generation)

llama-cli \
  -m Xing4.0-29B-A4B.APEX-I-MiniPlus-V2.1-Abliterated.gguf \
  -ngl 99 \
  -c 32768 \
  --temp 0.7 \
  --top-p 0.90 \
  -p "<|im_start|>system\nYou are an expert uncensored assistant.<|im_end|>\n<|im_start|>user\nExplain the architecture of multi-head latent attention.<|im_end|>\n<|im_start|>assistant\n"

2. llama-server (OpenAI-Compatible API)

llama-server \
  -m Xing4.0-29B-A4B.APEX-I-MiniPlus-V2.1-Abliterated.gguf \
  --port 8080 \
  --host 0.0.0.0 \
  -ngl 99 \
  -c 65536 \
  --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.