Qwen3.6-35B-A3B-MTP-APEX-I-MiniPlus-V2.1-Abliterated-GGUF

[!NOTE]

NEW: QWEN3.8 27B DENSE — VAL-APEX-I

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Independently computed using dedicated cloud compute clusters, funded 100% out-of-pocket.

If you consider these releases to be of high quality and they help you in your day-to-day workflow, consider supporting on Ko-fi. Every contribution directly sustains future cloud compute hours and helps support my university studies.

Independently computed on private cloud clusters. If this handcrafted release saves you VRAM and runs faster on your GPU, consider fueling the community compute fund on Ko-fi.

Explore the new canonical V3 releases. MiniPlus prioritizes Q5-class to near-Q6 fidelity without a fixed file-size ceiling; NanoPlus targets Q4-class quality at a practical footprint.

• 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).
• Swift-1.5 Qwen3.8 27B Uncensored MTP — MiniPlus V3 — 14.28 GB (+5.70% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S).
• Swift-1.5 Qwen3.8 27B Uncensored MTP — NanoPlus V3 — 11.08 GB (+2.26% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S).

Percentages compare the reported WikiText-2 PPL with GSQ-RCO IQ3_S (7.07). The linked 27B cards document the evaluation protocol and model variants.

Explore the APEX-I MiniPlus V2.1 Abliterated collection

EXPLORE THE ESTABLISHED 35B MoE MINIPLUS LINEUP

These are complementary APEX-I-MiniPlus V2.1 releases, not alternate downloads of the same model. Each receives the same tensor-by-tensor approach, integrated MTP where supported, and a design suitable for full or partial system-RAM inference. Choose the model whose native strengths best fit the work you want to do:

• Qwen3.6-35B-A3B MTP APEX-I-MiniPlus-V2.1 — a versatile frontier MoE for broad reasoning, multilingual work, agents, tool use, and multimodal tasks.
  • Best for: General reasoning, agent workflows, tool calling, and flexible multimodal use.
• Ornith 1.5 APEX-I-MiniPlus-V2.1 — a software-engineering-focused MoE designed for repository-scale coding and autonomous engineering agents.
  • Best for: Repository-scale development, multi-file code changes, and software-engineering agents.
• Tiel Coder APEX-I-MiniPlus-V2.1 — a specialist coding MoE tuned for agentic programming, iterative tool use, and implementation-heavy work.
  • Best for: Focused coding sessions, iterative debugging, and tool-driven implementation.

These remain distinct model families and editions with their own behavior and empirical results. Pick by workload and intended alignment behavior rather than treating them as interchangeable quantization variants.

<a id="quick-navigation"></a>Quick Navigation Index

1. Optimization History & Transparency Notice
2. Independent Benchmark of the APEX-I-MiniPlus Family (Occamy V2 Reference)
3. Empirical Benchmarks & Fidelity Verification
4. Quality Spectrum: APEX-I-MiniPlus V2.1 vs. Standard Flat Quantizations
5. Model Files & Technical Specifications
6. Surgical Tensor Quantization Map (Audited from GGUF)
7. Hardware Throughput & Offload Benchmarks (RTX 30 / 40 / 50 & RAM Streaming)
8. The 24GB Miracle: Full 256K Context Runs In VRAM!
9. Recommended Configuration & Setup
10. Recommended Generation Parameters (Qwen Official)
11. CRITICAL: Coding Syntax & Repeat Penalty Advisory (Preventing Character Swapping)
12. Hardened Agentic Chat Template & Reasoning Effort
13. Optional Support

Qwen3.6-35B-A3B-MTP APEX-I-MiniPlus-V2.1 Abliterated GGUF (Uncensored / Zero Refusal)

The Definitive Uncensored Frontier MoE · True Directional Refusal Ablation · Dedicated Q8_0 MTP & Vision Companions Included · Full 256K Context

[!NOTE]

🛡️ LOOKING FOR THE STANDARD ALIGNED RELEASE?

If you prefer the standard safety-aligned edition with default guardrails, check out the official base release: 🔗 Qwen3.6-35B-A3B-MTP-APEX-I-MiniPlus-V2.1-GGUF — 15.23 GB footprint, Q5_K_L fidelity (bordering Q6_K), zero AVX2 CPU stalls, and companion mmproj vision projector.

[!TIP]

🔓 AUTHENTIC HERETIC TPE DIRECTIONAL ABLITERATION (UNCENSORED)

This is the official authentic Abliterated / Uncensored edition of Qwen3.6-35B-A3B APEX-I-MiniPlus V2.1.

• Zero Refusal Vector: Refusal directions across system security, penetration testing, reverse engineering, and red-teaming tasks have been mathematically ablated via TPE multivariate Pareto optimization.
• Orthogonal Residual Steering: The refusal vector was orthogonalized against harmless activations and subtracted strictly from residual projections (ttn.o_proj and mlp.down_proj), guaranteeing that DeltaNet SSM recurrent state matrices remain pristine.
• Uncompromised Reasoning: Reasoning, code synthesis, mathematical derivations, and chain-of-thought pathways retain full fidelity without quantization degradation or lexical degradation.

[!IMPORTANT]

THE DEFINITIVE SPECIFICATION IN THE 14–15 GB CEILING

This APEX-I-MiniPlus-V2.1 release represents the specialized tensor-by-tensor configuration for sparse Mixture-of-Experts quantization within a 14–15 GB envelope. Every tensor across its 40 main layers and 256 micro-experts has been mathematically allocated to maximize reasoning precision, preserve routing behavior, and prevent avoidable CPU dequantization stalls. MTP draft models are provided as separate companion GGUFs.

[!TIP]

🏆 QUANTIZATION FIDELITY & ABLITERATION TRANSPARENCY

Honest Engineering Metrics: Q5_K_S / Q5-Limit Quality at a Featherweight 3-Bit Footprint

• Empirical WikiText-2 Perplexity: 5.4595 ± 0.12874 — ΔPPL +0.1395 (+2.62%) versus the approx. 5.32 uncompressed BF16 baseline.
• Transparent Quality-Tier Shift: The standard aligned release records ΔPPL +0.0493 (+0.93%), placing it in the Q5_K_L tier (bordering Q6_K). In this Abliterated edition, full directional refusal removal across residual-stream matrices raises the measured delta to +0.1395 PPL points, placing the model at the Q5_K_S / Q5-limit tier.
• Q5 Quality at Less-Than-Q3_K_M Footprint: Standard homogeneous Q5_K_M weighs approx. 25–26 GB. This build delivers that Q5-limit-tier coherence, code generation, and complex reasoning while occupying only 14.66 GB (13.65 GiB) — a 44% reduction in footprint over Q5_K_M, fitting entirely into a single 16GB consumer GPU.
• Zero Routing Drift: All 80 routing matrices (gate_inp and gate_shexp) remain in uncompressed F32, ensuring tokens are dispatched to the exact right experts on every forward pass without routing degradation.

[!TIP]

🏆 EMPIRICAL BENCHMARK & QUALITY COMPARISON

Quantization Specification	File Size (Disk)	Memory Footprint (RAM/VRAM)	Average BPW	WikiText-2 Perplexity	Quality Tier Equivalent
Unquantized BF16 Base	71.05 GB	66.18 GiB	16.00 BPW	approx. 5.32 (Reference)	Full precision baseline
APEX-I-MiniPlus V2.1 (Aligned)	15.23 GB	14.18 GiB	3.43 BPW	5.3693 ± 0.12528 (ΔPPL +0.0493 / +0.93%)	Q5_K_L tier (bordering Q6_K)
APEX-I-MiniPlus V2.1 Abliterated (CURRENT)	14.66 GB	13.65 GiB	3.38 BPW	5.4595 ± 0.12874 (ΔPPL +0.1395 / +2.62%)	Q5_K_S / Q5-limit tier
APEX-I-NanoPlus	13.03 GB	12.14 GiB	2.93 BPW	5.5244 ± 0.12916 (ΔPPL +0.2044 / +3.84%)	Solid Q4_K_M tier

Routing: all recipe-designated gate_inp and gate_shexp tensors remain in uncompressed F32, preserving zero routing drift.

• Q6_K-Bordering Tier in Language Fidelity: WikiText-2 perplexity delta is exceptionally low (under +0.05 / < 0.9% vs. BF16 on aligned weights), placing overall language representation at the boundary of a 28 GB Q6_K build within a approx. 14.66–15.23 GB footprint.
• Q5_K_L Tier in MoE Foundation Knowledge: All 120 shared expert tensors (shexp) across all 40 layers run in uncompressed Q5_K, keeping core domain knowledge intact across 100% of tokens.
• Protected Routing & Attention: Full quadratic attention Q/K/V is stored in Q3_K, while the attention output projections and global output head remain protected in Q6_K.

[!WARNING]

DO NOT CONFUSE APEX-I-MINIPLUS WITH GENERIC COMMUNITY APEX-I-MINI!

Regardless of release version, NEVER confuse handcrafted APEX-I-MiniPlus builds with generic community APEX-I-Mini releases:

• Generic Community APEX-I-Mini: Uniformly compresses all core MoE experts down to aggressive 2-bit IQ2_S, leaves the sensitive token output head unarmored at 3-bit Q3_K_M, and compresses attention projections down to Q3_K. In deep reasoning models, this triggers severe perplexity spikes, syntax errors, and broken code brackets.
• 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 gates in Q8_0, and keeps core reasoning experts at calibrated 3-bit treatment.

[!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 128K or more context entirely in VRAM can place the remaining model and context load in system RAM, maintaining stable, responsive generation at longer context lengths.

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<a id="toc-01"></a>

Optimization History & Transparency Notice

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

[!TIP]

Deployment & System Architecture Guide

• Full GPU VRAM Offload (24GB+ VRAM, -ngl 99): Effortless full offload with native 256K context support. Blistering throughput on RTX 3090 / 4090 / 5090 GPUs.
• System RAM Streaming Specialist (DDR4/DDR5 & Massive Context): Specially engineered to run either partially or entirely out of system RAM across large or full (+160k to 256k) context windows. By replacing non-linear codebooks with linear SIMD-optimized Q3_K edge experts and upgrading shared foundation experts to Q5_K across all 40 layers, AVX2 CPU dequantization stalls are completely eliminated. Depending on your processor architecture and memory bandwidth (dual-channel DDR4 or high-speed DDR5 6000+ MT/s), streaming generation in system RAM can approach speeds remarkably close to full VRAM execution, allowing both the dedicated Q8_0 multimodal vision projector (mmproj) and the integrated Multi-Token Prediction tensors to be used by compatible runtimes for speculative decoding and the Q8_0 multimodal projector (mmproj) to be loaded in GPU VRAM for OCR while the main model weights stream effortlessly from system RAM.

Explore the complete family of APEX-I-MiniPlus models in our official collection: APEX-I-MiniPlus V2.1 Hub Collection.

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<a id="independent-benchmark"></a> <a id="toc-02"></a>

🏅 Independent Benchmark of the APEX-I-MiniPlus Family (Occamy V2 Reference)

[!NOTE] External report: zephel01 independently benchmarked Occamy V2. The benchmark below was performed on Occamy-1.0 APEX-I-MiniPlus V2, not on this specific V2.1 model. It is included as independent evidence of the broader MiniPlus quantization approach.

The APEX-I-MiniPlus quantization architecture powering this model was subjected to an extensive independent evaluation by Japanese AI researcher and evaluator zephel01 (CoolZero) on an NVIDIA RTX 5090 (32GB) workstation running llama.cpp CUDA b11027 with FlashAttention (-fa on -ctk q8_0 -ctv q8_0 -ngl 99).

The evaluation tested the APEX-I hybrid MoE engine across 348 unseeded trials on SWE-bench style multi-file Python bug-fixing tasks with hidden pytest suites (llmbench):

• L6 Multi-File Code Generation (60 tasks):
  • Context 32,768 (32K): 93.3% Resolved (46/60 tasks passed 5/5 consecutive trials; 20/20 on Easy–Hard).
  • Context 65,536 (65K): 90.0% Resolved (45/60 tasks passed 5/5 consecutive trials).
  • Match with 25–28 GB Models: Matches or exceeds the resolution rate of full 25–28 GB models (such as Ornith-1.5 and Tiel-Coder 35B-A3B) while consuming over 10 GB less VRAM (14.6 GB vs approx. 26 GB).
• Extreme Context VRAM Scaling (The Hybrid DeltaNet SSM Advantage):
  • 32K Context: 14.6 GB total VRAM allocation.
  • 65K Context: 15.1 GB total VRAM allocation (only +0.5 GB VRAM added when doubling context!).
  • Architectural Explanation: Because 30 of the 40 layers utilize Linear Attention / DeltaNet SSM (O(1) constant recurrence memory), only the 10 full-attention anchor layers expand the KV cache. This proves empirically that 65,536 context runs 100% in VRAM on consumer 16GB GPUs (RTX 4080 / RTX 5080) without offloading to system RAM.
• Measured Real-World Throughput: Sustained single-stream generation of approx. 247 – 251 tok/s on NVIDIA RTX 5090.

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<a id="model-specifications"></a> <a id="empirical-benchmarks"></a> <a id="toc-03"></a>

Empirical Benchmarks & Fidelity Verification

The comparison table near the top consolidates the model-specific BF16 baseline, final GGUF PPL, delta, published main-file size, BPW, and fidelity tier. The routing treatment is preserved in the note directly beneath it.

<a id="quality-spectrum"></a> <a id="toc-04"></a>

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 35B Mixture-of-Experts architectures:

<a id="toc-05"></a>

Model Files & Technical Specifications

• Base Model: Qwen/Qwen3.6-35B-A3B
• Parameters: 35.2B total (approx. 2.6B to 3.2B active per token)
• Architecture: 40 layers, 256 micro-experts (8 active per token) + hybrid linear attention / DeltaNet recurrent layers
• Context Length: 262,144 tokens (native 256K)

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<a id="tensor-map"></a> <a id="toc-06"></a>

Surgical Tensor Quantization Map (Audited from GGUF)

The exact tensor breakdown below has been verified directly from the compiled binary weights:

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<a id="throughput-projections"></a> <a id="toc-07"></a>

Hardware Throughput & Offload Benchmarks (RTX 30 / 40 / 50 & RAM Streaming)

Empirically verified in Unsloth Studio & llama.cpp:

• Aggressive Hybrid Offload Profile: Hybrid offload supports reasoning-enabled generation with limited VRAM while the remaining model weights stream from system RAM.

[!NOTE]

Empirical Testbed Architecture & Desktop/Server Scaling

• Empirical Benchmark Hardware: The hybrid offload and system RAM streaming behavior documented above was measured on a consumer laptop powered by an Intel 12th Gen Alder Lake architecture featuring a hybrid design of Performance Cores (P-Cores) and Efficient Cores (E-Cores) paired with dual-channel system RAM and constrained laptop power/thermal envelopes.
• Thread Scheduling & E-Core Contention: In hybrid architectures like Alder Lake, OS thread scheduling across background E-Cores and lower single-core mobile power limits introduce memory bandwidth and thread synchronization overhead during CPU dequantization.
• Dramatic Scaling on Higher-End Processors: When running on desktop or server processors (such as modern AMD Ryzen 7000 / 9000 Zen 4/5 series or high-TDP Intel desktop platforms with dedicated performance cores, large L3 caches, and high-bandwidth dual- or quad-channel DDR5 running at 6000+ MT/s), streaming generation speeds and prefill throughput will scale dramatically higher, substantially exceeding these measured mobile numbers.

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<a id="context-scaling"></a> <a id="toc-08"></a>

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

Qwen3.6-35B-A3B-MTP APEX-I-MiniPlus-V2.1 fits the entire 256K context window within 24GB VRAM:

Note: Leaves comfortable headroom for display drivers and compute buffers on standard 24GB GPUs (RTX 3090, RTX 4090, RTX 5090).

[!TIP]

💡 Empirical 16GB GPU Verification (Single Stream / Desktop)

While theoretical multi-slot server buffers estimate approx. 16.5 GiB, independent hardware testing by zephel01 on an RTX 5090 (Occamy V2 Reference) confirmed that single-stream desktop inference consumes only 14.6 GB at 32,768 ctx and only 15.1 GB at 65,536 ctx (-ctk q8_0 -ctv q8_0 -fa on). This empirically proves that full 65K context runs completely in VRAM on 16GB cards (RTX 4080 / RTX 5080) without system RAM offload!

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<a id="recommended-setup"></a> <a id="toc-09"></a>

Recommended Configuration & Setup

llama-server.exe \
 -m Qwen3.6-35B-A3B.APEX-I-MiniPlus-V2.1-Abliterated.gguf \
 --port 8080 \
 --parallel 4 \
 --flash-attn on \
 --fit on \
 -c 104960 \
 --cache-type-k q8_0 \
 --cache-type-v q8_0

<a id="generation-parameters"></a> <a id="toc-10"></a>

⚙️ Recommended Generation Parameters (Qwen Official)

Sampling metadata recorded from Qwen/Qwen3.6-35B-A3B in the completed build:

[!IMPORTANT] <a id="quantization-fidelity"></a>

🔍 Model Inherent Behavior vs. Quantization Fidelity Notice

Any behavioral nuances, stylistic tendencies, domain-specific habits, or zero-shot edge-case oversights stem entirely from the original unquantized checkpoint weights and fine-tuning distribution, NOT from the APEX-I quantization process. Handcrafted APEX-I-MiniPlus strictly preserves mathematical tensor fidelity—keeping 100% of expert routing matrices (gate_inp) in uncompressed F32 (zero router drift), armoring the token output head in Q6_K, and safeguarding attention gates in Q8_0. Empirical verification records the final GGUF perplexity at 5.4595 ± 0.12874, a ΔPPL +0.1395 (+2.62%) versus the approx. 5.32 BF16 baseline (Q5_K_S / Q5-limit tier post-abliteration).

<a id="toc-coding-advisory"></a> <a id="coding-advisory"></a>

[!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.

Verified Upstream Behavior: This self-correcting behavior (where the model notices the mistake in its thinking loop but repeats the substitution) is documented on official upstream base checkpoints and Q8 builds (Ornith Discussion #34 and Discussion #22). It is completely eliminated by proper sampling configuration:

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 ( { vs {, = vs =) cleanly.

<a id="toc-chat-template"></a> <a id="chat-template"></a>

[!TIP]

HARDENED AGENTIC CHAT TEMPLATE (JINJA)

An optimized chat_template.jinja is included at the root of this repository. It hardens agent workflows and multi-turn stability:

1. Native reasoning_effort Multi-Level 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 code, avoiding circular self-doubt loops.
   • none / off: Closes the thinking block immediately (<think>\n\n</think>) when reasoning is disabled.
2. Tool-Calling Safeguard (Anti-Premature Stop): Prevents the model from terminating a turn (<|im_end|>) at a colon or action declaration prior to outputting <tool_call>.
3. Multi-Turn Thinking Memory: Preserves historical <think> blocks across turns by default, preventing context distribution drift in 78K+ token runs.

Usage with llama-server:

llama-server -m Model.gguf --chat-template-file chat_template.jinja --reasoning-effort medium

<a id="toc-11"></a>

7. Community Compute Fund & Student Support

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All IsValorum quantizations will always remain completely free and open to the public without paywalls.

However, cloud GPU compute is expensive and funded entirely out-of-pocket. If you find these handcrafted builds to be of high quality and they benefit your day-to-day workflow, you can support future Day-0 releases on Ko-fi. Every contribution directly funds the cloud compute hours required for new model runs and helps support my university education:

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(When supporting on Ko-fi, feel free to leave a note with your Hugging Face handle and the specific model you would like prioritized).

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