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

The Uncensored 27B Frontier Reasoning Engine · Abliterated Base · Native 256K Context

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 QWEN3.8 27B VAL-APEX-I EDITIONS

Choose a model variant and quantization profile:

• Qwen3.8 27B — MiniPlus V3 — 14.16 GB (+13.21% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S).
• Qwen3.8 27B — NanoPlus V3 — 11.35 GB (+11.49% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S).
• Qwen3.8 27B EfficientThink Uncensored — MiniPlus V2.1 — 15.08 GB (+15.01% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S).
• Qwen3.8 27B EfficientThink Uncensored — NanoPlus — 11.65 GB (+10.87% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S).
• Qwen3.8 27B Huihui Abliterated — MiniPlus V2.1 — 15.33 GB (+13.06% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S).

VAL-APEX-I collection · MiniPlus collection · NanoPlus collection

[!IMPORTANT]

THE DEFINITIVE SPECIFICATION IN THE 11 to 12 GB CEILING

This VAL-APEX-I NanoPlus release is 11.90 GB, with 3.48 whole-file BPW and 866 audited tensors. Its reported WikiText-2 PPL is 6.4238 ± 0.4960, or +0.6296 (+10.87%) relative to the unmodified BF16 Base reference. This delta includes abliteration as well as quantization.

Quick Navigation Index

1. Quantization Comparison: Metrics & Tensor Map
2. Model Files & Technical Specifications
3. Native Context & Runtime Memory
4. Recommended Configuration & Setup
5. Recommended Generation Parameters
6. CRITICAL: Coding Syntax & Repeat Penalty Advisory (Preventing Character Swapping)
7. Hardened Agentic Chat Template & Reasoning Effort
8. Optional Support

1. Quantization Comparison

Size & Quality Metrics

The NanoPlus V3 file is 11.35 GB, approximately 0.45 GB smaller than GSQ-RCO IQ3_S, with 11.49% lower reported WikiText-2 PPL (6.2577 vs 7.07). MiniPlus V3 reaches 13.21% lower reported PPL (6.1361 vs 7.07) at 14.16 GB, approximately 2.36 GB larger.

Tensor Precision Map

× is the number of tensors in each format. Counts were read from the released GGUF headers; the GSQ-RCO column uses its published tensor allocation.

This release contains 866 tensors. The main GGUF includes the additional 15-tensor MTP head.

2. 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 main hybrid layers (48 Gated DeltaNet SSM linear attention layers + 16 periodic full quadratic attention layers), plus an integrated MTP head at blk.64.*
• 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.

3. Native Context & Runtime Memory

The model supports a native context window of 262,144 tokens. Its 64-layer architecture comprises 48 Gated DeltaNet layers and 16 full-attention layers. Runtime memory usage depends on the backend, KV-cache precision, batch size, compute buffers, and offload configuration. Check the runtime memory allocation for your chosen settings.

4. 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.
4. Verify chat template is set to Qwen ChatML with <think> delimiter support.

5. Recommended Generation Parameters

6. 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.

7. 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.

8. 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.