base_model: nerkyor/Qwen3.8-27B-EfficientThink-Uncensored-K3-Opus5-Grok4.6-GPT5.6Sol-SFT-SimPO-DFlash2 base_model_relation: quantized quantized_by: IsValorum library_name: gguf license: apache-2.0 language:

• en tags:
• gguf
• llama.cpp
• val-apex-i
• quantization
• qwen3.8
• reasoning
• cot
• uncensored
• simpo
• dflash2
• text-generation pipeline_tag: text-generation

Qwen3.8-27B-EfficientThink-Uncensored VAL-APEX-I MiniPlus V2.1 GGUF

The SimPO + DFlash2 Frontier Reasoning Specialist · Synthetic SFT (Opus 5 / Grok 4.6 / GPT 5.6 Sol) · Full 256K Context on 24GB GPUs

Official VAL-APEX-I MiniPlus V2.1 quantization of nerkyor/Qwen3.8-27B-EfficientThink-Uncensored-K3-Opus5-Grok4.6-GPT5.6Sol-SFT-SimPO-DFlash2.

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

Explore the complete multi-model series in the VAL-APEX-I Collection on Hugging Face.

Quick Navigation Index

• 1. Optimization History & Transparency Notice
• 2. Upstream Lineage & Ecosystem Compatibility
• 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
• 10. Hardened Agentic Chat Template & Reasoning Effort
• 11. Optional Support

1. Optimization History & Transparency Notice

[!NOTE]

WHY VAL-APEX-I REPLACES LEGACY FORMATS

Traditional uniform quantizations (Q4_K_M, Q5_K_M) compress all tensors with broad brushstrokes, which damages hybrid architectures. Qwen3.8-27B combines 47 recurrent DeltaNet SSM layers with 17 periodic full-attention layers. Standard quantizers compress recurrent states (ssm_a, ssm_conv1d, ssm_dt, ssm_norm), introducing state-space drift that degrades deep reasoning traces and causes syntax collapse.

VAL-APEX-I fixes this structurally:

1. Preserves all 47 DeltaNet SSM state operators strictly in uncompressed F32.
2. Guards the global output.weight classification head in Q6_K to prevent reasoning token corruption and <think> delimiter failures.
3. Locks attention gating in Q8_0 to prevent crosstalk between linear and quadratic attention mechanisms.
4. Applies non-linear codebooks (IQ4_NL, IQ3_XXS) exclusively to dense MLP matrices where SiLU activation noise naturally cancels out.
5. Incorporates importance-matrix calibration from representative multi-domain datasets.

2. Upstream Lineage & Ecosystem Compatibility

This model is derived from nerkyor/Qwen3.8-27B-EfficientThink-Uncensored-K3-Opus5-Grok4.6-GPT5.6Sol-SFT-SimPO-DFlash2 and inherits its full ecosystem compatibility:

1. Training Lineage & Data Mixture

• Pretrained Base: Qwen3.8-27B hybrid linear-quadratic architecture.
• Supervised Fine-Tuning (SFT): Fine-tuned across deep chain-of-thought traces synthesized from high-capability frontier models:
  • Claude 3.5 / 3.7 Opus synthetic reasoning datasets
  • Grok 4.6 synthetic reasoning and code generation traces
  • GPT 5.6 Sol mathematical proofs and STEM derivations
• Preference Optimization: SimPO (Simple Preference Optimization) applied to align reasoning depth and enforce uncensored analytical compliance without refusal degeneration.
• Uncensored Nature: Free from corporate preachy refusals; directly answers technical, red-teaming, penetration testing, and controversial analytical prompts.

2. DFlash2 Multi-Token Prediction (MTP) Speculative Decoding

• Integrated Draft Head: Upstream integrates DFlash2 (Next-N speculative draft prediction) situated at layer 64 (blk.64.*).
• Parallel Candidate Drafting: DFlash parallelizes candidate token generation; the main model validates multiple tokens in a single forward pass, significantly reducing autoregressive latency.
• Compatibility with llama.cpp: Can be run in standard autoregressive mode, or with MTP speculative decoding where supported via --spec-draft-n-max 7 or --spec-type draft-mtp.

3. Serving Engine Support

• SGLang (Primary Upstream Tested Engine): Upstream author's primary benchmarked deployment. Supports SGLang DFlash2 verify block size 8.
• vLLM: Compatible with vLLM standard serving via --reasoning-parser qwen3 --max-model-len 40960.
• Lynn Agent (v0.87.0+): Full native compatibility for autonomous tool-use, multi-turn terminal execution, and repository-scale agent loops.
• llama.cpp / llama-server: 100% plug-and-play with official llama.cpp builds (b11000+) supporting hybrid DeltaNet SSM architectures.

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:

Prefer the ultra-compact edition? APEX-I-NanoPlus compresses the model to 11.65 GB for smooth operation on 16GB hardware, while MiniPlus V2.1 retains the higher 15.08 GB Q5_K_M foundation precision.

4. Model Files & Technical Specifications

• Base Model: nerkyor/Qwen3.8-27B-EfficientThink-Uncensored-K3-Opus5-Grok4.6-GPT5.6Sol-SFT-SimPO-DFlash2
• Parameters: 27B total dense hybrid
• Architecture: 64 hybrid layers (47 Gated DeltaNet SSM linear attention layers + 17 periodic full quadratic attention layers) + DFlash2 layer 64 draft head
• Context Length: 262,144 tokens (native 256K)
• Quantization Standard: VAL-APEX-I MiniPlus V2.1 calibrated against official Qwen3.8-27B importance matrix (imatrix)
• Ecosystem: SGLang, vLLM, Lynn Agent v0.87.0, llama.cpp

Empirical WikiText-2 Perplexity Benchmark

Evaluated empirically via llama-perplexity (WikiText-2 test set, 512 context length, 512 batch size):

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

5. Surgical Tensor Quantization Map (Audited from GGUF)

The exact tensor breakdown below has been verified directly from the compiled binary weights (851 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 Qwen3.8-27B-EfficientThink-Uncensored-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 Qwen3.8-27B-EfficientThink-Uncensored-VAL-APEX-I-MiniPlus-V2.1.gguf \
  --jinja \
  -ngl 99 \
  -c 32768 \
  -p "You are an expert systems programmer. Write an asynchronous TCP socket server in Rust with graceful shutdown.<|im_end|>\n<|im_start|>assistant\n<think>\n"

LM Studio / Ollama

• LM Studio: Drag and drop the downloaded Qwen3.8-27B-EfficientThink-Uncensored-VAL-APEX-I-MiniPlus-V2.1.gguf directly into LM Studio's model directory (.cache/lm-studio/models). In the sidebar settings, set Context Length to 32768 (or higher) and GPU Acceleration to Max.
• Ollama: Create a custom Modelfile:

  FROM ./Qwen3.8-27B-EfficientThink-Uncensored-VAL-APEX-I-MiniPlus-V2.1.gguf
  PARAMETER stop "<|im_end|>"
  PARAMETER stop "<|endoftext|>"
  PARAMETER temperature 0.60
  PARAMETER top_p 0.95
  PARAMETER min_p 0.05
  PARAMETER num_ctx 32768
  

  Then build and run:

  ollama create qwen3.8-27b-efficientthink -f Modelfile
  ollama run qwen3.8-27b-efficientthink
  

8. Recommended Generation Parameters

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

[!WARNING]

WHY MODERN REASONING MODELS SWAP CHARACTERS UNDER REPEAT PENALTY

In modern reasoning models (Qwen 2.5/3.5/3.8, DeepSeek R1/V3), repetitive structures are fundamental to correct output:

• Programming languages require repetitive syntax patterns (e.g., spaces, closing brackets }}}, repeated variable names, standard library imports).
• Chain-of-thought reasoning (<think> ... </think>) frequently refers back to the same hypothesis, equation, or variable name.

If you apply a traditional Repeat Penalty (1.1 - 1.3), the sampler artificially penalizes identical tokens. In code, this forces the model to substitute valid characters with erratic alternatives (e.g., changing letters, omitting punctuation, or breaking whitespace indentation).

[!IMPORTANT] 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.