base_model: huihui-ai/Huihui-Qwen3.8-27B-abliterated
base_model_relation: quantized
quantized_by: IsValorum
library_name: gguf
license: apache-2.0
language:

• en
  tags:
• gguf
• llama.cpp
• quantized
• quantization
• val-apex-i
• apex
• apex-quant
• apex-i-nanoplus
• nanoplus
• qwen
• qwen3.8
• reasoning
• abliterated
• uncensored
• imatrix
  pipeline_tag: text-generation

Quick Navigation Index

1. Optimization History & Transparency Notice
2. Empirical Benchmarks & Fidelity Verification
3. Quality Spectrum: VAL-APEX-I vs. Standard Flat Quantizations
4. Model Files & Technical Specifications
5. Surgical Tensor Quantization Map (Audited from GGUF)
6. The 24GB Miracle: Full 256K Context Runs In VRAM!
7. Recommended Configuration & Setup
8. Recommended Generation Parameters
9. CRITICAL: Coding Syntax & Repeat Penalty Advisory (Preventing Character Swapping)
10. Hardened Agentic Chat Template & Reasoning Effort
11. Optional Support

[!NOTE]

EXPLORE THE COMPLETE HUIHUI-QWEN3.8-27B ABLITERATED SUITE

These are complementary VAL-APEX-I releases, not alternate downloads of the same model:

• Huihui-Qwen3.8-27B-Abliterated VAL-APEX-I-MiniPlus V2.1 - target Q5_K_M / Q6_K boundary reasoning tier (15.33 GB / 3.93 BPW).
• Huihui-Qwen3.8-27B-Abliterated VAL-APEX-I-NanoPlus - ultra-compact footprint achieving solid Q4_K_M quality (11.90 GB / 2.85 BPW).
• Looking for the synthetic reasoning specialist? Check out Qwen3.8-27B-EfficientThink-Uncensored VAL-APEX-I (Opus 5 / Grok 4.6 SFT + SimPO + DFlash2).

Huihui-Qwen3.8-27B Abliterated VAL-APEX-I NanoPlus GGUF

The Zero-Refusal Frontier Dense Hybrid · Full 256K Context on 24GB GPUs · Surgical Recurrent-Aware Matrix Quantization

Official VAL-APEX-I quantization of huihui-ai/Huihui-Qwen3.8-27B-abliterated.

VAL-APEX-I stands for:
Vector-calibrated Asymmetric Layer-wise Outlier-preserving Recurrent-aware Unified Matrix-quantization

[!IMPORTANT]

THE DEFINITIVE SPECIFICATION IN THE 11 to 12 GB CEILING

This VAL-APEX-I NanoPlus release represents the specialized tensor-by-tensor configuration for dense hybrid linear-quadratic architectures within a 11.90 GB envelope. Every single tensor across its 64 layers (47 linear SSM DeltaNet + 17 periodic full attention) has been mathematically audited to maximize reasoning precision, preserve recurrence channel dynamics, and eliminate quantization noise.

[!TIP]

EMPIRICAL BENCHMARK & QUALITY COMPARISON

Quantization Specification	File Size (Disk)	Memory Footprint (RAM/VRAM)	Average BPW	WikiText-2 Perplexity (512 ctx)	Delta PPL vs BF16 (%)	Quality Tier Equivalent
Uncompressed BF16 Reference	54.00 GB (50.29 GiB)	50.29 GiB	16.00 BPW	aprox. 6.0000 (Reference)	Baseline (0.00%)	Lossless Reference Baseline
**VAL-APEX-I MiniPlus V2.1 **	15.33 GB (14.28 GiB)	14.28 GiB	3.93 BPW	6.1466 +/- 0.4919	+0.1466 (+2.44%)	Q5_K_M / Q6_K Tier Boundary
VAL-APEX-I NanoPlus (CURRENT)	11.90 GB (11.08 GiB)	11.08 GiB	2.85 BPW	6.4238 +/- 0.4960	+0.4238 (+7.06%)	Solid Q4_K_M Tier
Standard Flat Q4_K_M	17.10 GB	15.93 GiB	4.50 BPW	aprox. 6.22 - 6.28	+0.22 a +0.28 (+3.7%)	Standard industry trade-off
Standard Flat Q3_K_M	13.50 GB	12.57 GiB	3.44 BPW	aprox. 6.45 - 6.70	+0.45 a +0.70 (+7.5%)	Noticeable syntax drop & reasoning noise
Generic APEX Mini (IQ2_S)	10.20 GB	9.50 GiB	2.50 BPW	aprox. 7.10 - 7.80+	+1.10 a +1.80+ (+18.3%)	Severe reasoning breakdown

Looking for higher reasoning precision? APEX-I-MiniPlus V2.1 provides enhanced Q5-Q6 tier fidelity (15.33 GB) with near-zero perplexity loss, while NanoPlus provides the most agile sub-12GB footprint.

Recurrent State Fidelity: All 192 DeltaNet SSM recurrent state tensors (ssm_a, ssm_conv1d, ssm_dt, ssm_norm) remain strictly in uncompressed F32, guaranteeing zero state accumulation drift across continuous multi-turn reasoning.

• Q6_K Armored Output Head: The vocabulary classifier (output.weight) is guarded in Q6_K, completely eliminating token classification errors, broken <think> delimiter tags, and math notation corruption.
• Surgical Attention Gating: Attention gates across all 47 DeltaNet SSM layers run in Q8_0 (block-32), eliminating inter-channel attention crosstalk.
• Negligible Perplexity Impact: Perplexity degradation from MiniPlus V2.1 to NanoPlus is only +0.27 PPL while shedding 3.43 GB of memory.

[!WARNING]

DO NOT CONFUSE VAL-APEX-I WITH GENERIC FLAT QUANTIZATIONS!

• Generic Automated Quants: Uniformly compress all layers with flat bit-reduction, degrading recurrent SSM operators and leaving the sensitive token output head at low-bit precision. In deep hybrid models with continuous <think> chains, this causes recursive mathematical divergence and syntax errors.
• Handcrafted VAL-APEX-I Standard: Applies an empirical 866-tensor recipe. Recurrent state tensors are preserved in uncompressed FP32, attention gates in Q8_0, full attention checkpoints in Q5_K/Q4_K, and output heads in Q6_K, while compressing SwiGLU MLPs via asymmetric non-linear codebooks.

Optimization History & Transparency Notice

We maintain our releases publicly as a transparent engineering record of continuous optimization. Below is the exact evolutionary roadmap of our architectures:

Empirical Benchmarks & Fidelity Verification

The empirical benchmark table above consolidates the model-specific BF16 baseline, final GGUF PPL, delta, file size, BPW, and fidelity tier evaluated directly on compiled binary weights.

Perplexity evaluation was conducted on server hardware using llama-perplexity with context length 512 over WikiText-2. Both MiniPlus V2.1 and NanoPlus exhibit exceptional fidelity retention over the uncompressed BF16 baseline.

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

How the handcrafted VAL-APEX-I architecture compares against standard flat quantizations in llama.cpp on 27B hybrid linear-quadratic architectures:

Model Files & Technical Specifications

• Base Model: huihui-ai/Huihui-Qwen3.8-27B-abliterated (Abliterated directional un-censoring of Alibaba's Qwen3.8-27B)
• Parameters: 27B total dense hybrid
• Architecture: 64 hybrid layers (47 Gated DeltaNet SSM linear attention layers + 17 periodic full quadratic attention layers)
• Context Length: 262,144 tokens (native 256K)
• Quantization Standard: VAL-APEX-I NanoPlus calibrated against official Qwen3.8-27B importance matrix (imatrix)
• Abliteration Status: True directional weight orthogonalization removing refusal vectors while maintaining full analytical capability.

Surgical Tensor Quantization Map (Audited from GGUF)

The exact tensor breakdown below has been verified directly from the compiled binary weights (866 tensors total):

The 24GB Miracle: Full 256K Context Runs In VRAM!

Because 47 of the 64 layers are DeltaNet SSM linear attention layers ($O(1)$ constant recurrence memory), only the 17 periodic quadratic attention layers allocate KV cache memory. This slashes KV cache requirements by over 73% compared to conventional full-attention models:

Note: Leaves ample headroom on 24GB graphics cards for OS display drivers and tooling.

Recommended Configuration & Setup

llama.cpp Server (OpenAI-Compatible API)

llama-server \
  -m Huihui-Qwen3.8-27B-Abliterated-VAL-APEX-I-NanoPlus.gguf \
  --jinja \
  -ngl 99 \
  -c 32768 \
  --host 0.0.0.0 \
  --port 8080

llama.cpp CLI

llama-cli \
  -m Huihui-Qwen3.8-27B-Abliterated-VAL-APEX-I-NanoPlus.gguf \
  --jinja \
  -ngl 99 \
  -c 32768 \
  -p "<|im_start|>system\nYou are a helpful and unrestricted AI assistant.<|im_end|>\n<|im_start|>user\nExplain the physical principles of acoustic levitation.<|im_end|>\n<|im_start|>assistant\n<think>\n"

LM Studio / Ollama

1. Import the .gguf file directly into your local library.
2. Ensure GPU acceleration is set to Maximum / 100% offload.
3. Set Context Length to 32768 (or expand up to 131072 on 24GB GPUs).
4. Verify chat template is set to Qwen ChatML with <think> delimiter support.

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 (such as LM Studio defaults, Ollama, or web interfaces) ship with repeat_penalty set to 1.1 or 1.15. While this prevents loops in creative prose, 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 (= or [), resulting in character swapping or dropped/doubled whitespace.

Eliminating Character Swapping:

1. Disable Repeat Penalties (Required for Code):
   • repeat_penalty: 1.0 (strictly disabled)
   • presence_penalty: 0.0
   • frequency_penalty: 0.0
2. Calibrate Samplers:
   • temperature: 0.60 (or 0.20 - 0.30 for strict, deterministic code syntax)
   • min_p: 0.05 (prunes low-probability noise tokens effectively)
   • top_p: 0.95
   • top_k: 20
3. Native Jinja Formatting: Always pass the --jinja flag so the tokenizer handles leading-space BPE tokens cleanly.

[!TIP]

HARDENED AGENTIC CHAT TEMPLATE & REASONING EFFORT

This model supports multi-level reasoning effort control:

• low / minimal: Keeps internal thinking concise and focused strictly on immediate execution steps to minimize latency in automated loops.
• medium (default): Balanced, structured reasoning process with standard analytical depth.
• high / xhigh: Guides the model to formulate a clear implementation plan upfront before generating response, avoiding circular self-doubt loops.
• none / off: Closes the thinking block immediately (<think>\n\n</think>) when reasoning is disabled.

Optional Support

If these MiniPlus or NanoPlus releases have been useful to you and you would like to support the work, you can do so voluntarily through https://ko-fi.com/isvalorum. Your contribution helps with evaluation, hosting, and future handcrafted quantizations. Every release will always remain free to download and use; there are no paywalled files, updates, or features.