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-miniplus
• v2.1
• qwen
• qwen3.8
• reasoning
• abliterated
• uncensored
• imatrix pipeline_tag: text-generation

Huihui-Qwen3.8-27B Abliterated VAL-APEX-I MiniPlus V2.1 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

[!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).
• Explore the full lineup in the official VAL-APEX-I Collection or the APEX-I-MiniPlus V2.1 Collection / APEX-I-NanoPlus Collection.

[!IMPORTANT]

THE DEFINITIVE SPECIFICATION IN THE 15 GB CEILING

This VAL-APEX-I MiniPlus V2.1 release represents the specialized tensor-by-tensor configuration for dense hybrid linear-quadratic architectures within a 15.33 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.

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

1. 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:

2. Empirical Benchmarks & Fidelity Verification

Evaluated directly on the compiled GGUF binary weights using llama-perplexity with context length 512 over the standard WikiText-2 benchmark:

Note: The uncompressed BF16 reference (6.0000 PPL) is derived from established community benchmarks on WikiText-2 (512 ctx) for Qwen3.8-27B.

Prefer the ultra-compact edition? APEX-I-NanoPlus compresses the model to 11.90 GB for smooth operation on 16GB hardware, while MiniPlus V2.1 retains the higher 15.33 GB Q5_K_M foundation precision.

Key Empirical Takeaways:

• 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.
• Minimal Perplexity Degradation: Perplexity degradation from MiniPlus V2.1 to NanoPlus is only +0.27 PPL while shedding 3.43 GB of memory.

3. 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:

4. 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 MiniPlus V2.1 calibrated against official Qwen3.8-27B importance matrix (imatrix)
• Abliteration Status: True directional weight orthogonalization removing refusal vectors while maintaining full analytical capability.

5. Surgical Tensor Quantization Map (Audited from GGUF)

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

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

Because 47 of the 64 layers are DeltaNet SSM linear attention layers (constant O(1) 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.

7. Recommended Configuration & Setup

llama.cpp Server (OpenAI-Compatible API)

llama-server \
  -m Huihui-Qwen3.8-27B-Abliterated-VAL-APEX-I-MiniPlus-V2.1.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-MiniPlus-V2.1.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.

8. Recommended Generation Parameters

9. CRITICAL: Coding Syntax & Repeat Penalty Advisory (Preventing Character Swapping)

[!IMPORTANT]

PREVENTING SYNTAX & TOKEN SWAPPING IN CODE WORKFLOWS

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.

10. Hardened Agentic Chat Template & Reasoning Effort

[!TIP]

MULTI-LEVEL REASONING EFFORT CONTROL

This model supports multi-level reasoning effort control via the Jinja template:

• 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 when reasoning is disabled.

11. 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.