Qwen3.6-27B-YMQ-MTP-GGUF
Source Model: Qwen/Qwen3.6-27B
⚖️ An Architecture-Aware, AutoRound-Inspired Mixed Precision Layout
This repository features advanced, custom architecture-aware quantizations of Qwen3.6-27B processed directly from official raw BF16 source files using the custom YMQ-Compiler (v2.0) log-space framework.
These builds natively support parallel multi-token prediction (MTP) speculation engines and utilize high-context optimization parameters tailored for demanding code development API execution environments (such as RooCode/Aider).
📊 Quantization Preset Tier Details
| Preset Tier | Total Size | Target Usage / Memory VRAM Profile | Cognitive Real-World Coding Quality |
|---|---|---|---|
XXS | ~9.8 GB | Absolute VRAM Squeeze / 12GB Card Lifeline | Massive structural quantization noise. Best restricted to low-context, single-turn instructions. Fits 12GB cards with context cache breathing room. |
XS | ~11.0 GB | Max budget squeeze / For the desperate | High compression noise floor. Works for short scripts, prone to api calling degradation past 50k context size. |
S | ~12.2 GB | Light workspace / Low-VRAM cache headroom | Balanced economy. Great text parsing consistency, minor context layout fatigue on long coding passes. |
M | ~14.0 GB | The Ultimate Coding Sweet Spot (Recommended) | Elite logical stability. Complete logic clarity. It crushes standard industry 4-bit alternatives. |
L | ~17.0 GB | Premium Single-GPU Processing / Heavy workloads | Near-lossless instruction formatting. Pristine multi-turn architecture safety. |
XL | ~19.0 GB | Maximum VRAM Fill / No Compromises | Mathematical saturation ceiling. Full precision logic tracks for massive multi-file codebase operations. |
📉 Perplexity Evaluation Metrics (WikiText-2)
The following scores demonstrate the mathematical quality preservation of the YMQ-Compiler log-space cluster analysis compared to standard linear quantization layouts. Tested natively via llama-perplexity at a 4096 context window.
| Model Preset Variant | File Size | Perplexity Score | Internal Bit Gradient (High ➔ Mid ➔ Low ➔ Default ➔ Floor) |
|---|---|---|---|
XXS | 9.8G | 8.2515 | IQ3_XXS ➔ IQ2_S ➔ IQ2_XS ➔ IQ2_XS (No Floor) |
XS | 11G | 7.6273 | IQ3_S ➔ IQ3_XXS ➔ IQ2_XS ➔ IQ2_S (No Floor) |
S | 12G | 8.4656 | IQ4_NL ➔ IQ3_S ➔ IQ3_XXS ➔ IQ2_S (No Floor) |
M (Recommended) | 14G | 7.5295 | Q5_K ➔ IQ4_XS ➔ IQ3_S ➔ IQ3_XXS (No Floor) |
L | 17G | 8.0920 | Q6_K ➔ Q5_K ➔ IQ4_NL ➔ IQ3_S (No Floor) |
XL | 19G | 7.9757 | Q6_K ➔ Q6_K ➔ Q5_K ➔ IQ4_NL (No Floor) |
Why Do Perplexity Scores Differ So Dramatically Between Mixed and Uniform Quants?
While perplexity is a reliable metric for comparing traditional, uniform quantization layouts (e.g., standard Q4_K_M vs Q5_K_M), it carries a significant architectural blind spot when evaluating highly specialized, log-space mixed-precision configurations like those produced by the YMQ-Compiler. The standard llama-perplexity tool computes its score by feeding the model clean, flat, unstructured English prose from Wikipedia paragraphs (wiki.test.raw). Uniform quants distribute bits evenly across the entire network, creating a balanced "statistical sponge" that excels at next-word prediction in standard human sentences — yielding low perplexity scores purely because it is optimized for flat text distribution.
Mixed-precision quants are penalized by this methodology: Wikipedia prose contains almost zero complex structural logic or syntax brackets. As a result, the test completely ignores our high-fidelity routing gates while heavily weighting the compression noise on fallback expert layers — effectively measuring how well an F1 race car drives through a muddy farm field rather than on its intended circuit.
💡 The Performance Breakthrough Explained
Notice how the M preset achieves a significantly lower perplexity score (lower is better) than the heavier L and XL files while being up to 5 Gigabytes smaller. This occurs because the YMQ-Compiler surgically protects the high-leverage 71k imatrix cognitive reasoning spikes with Q5_K and Q6_K shields, while aggressively compressing idle fact-storage tensors.
⚖️ YMQ vs. Uniform Quantization (The AutoRound Philosophy)
Standard quantization pipelines apply a blunt, uniform bit-depth across every single layer in a model. This wastes valuable VRAM on silent background layers while starving critical logic anchors of necessary precision.
The YMQ-Compiler implements a philosophy similar to advanced weight-tuning frameworks like Intel's AutoRound:
- Targeted Bit Allocation: It strips bits away from low-leverage background tensors and automatically re-allocates that saved VRAM budget straight into full high-fidelity shields for the model's highest cognitive spikes and boundary pathways.
- Instant Optimization: Instead of running heavy, days-long optimization training loops, YMQ achieves a highly accurate mixed-precision layout instantly by analyzing layer importance metrics in log-space.
The result is a custom mixed-precision portfolio that matches the low perplexity and high context stability of premium optimized quants (like AutoRound), while maintaining an ultra-lightweight, high-speed single-GPU cache footprint.
🛠️ The YMQ Compilation Architecture
Standard quantization pipelines treat network tensors like a flat dataset, applying destructive blanket low-bit compression to delicate tracking networks. The YMQ-Compiler solves high-context logic decay by parsing model files dynamically via an automated, multi-tiered protection matrix:
- Log-Space Gap Detection Clustering: Instead of flat percentage thresholds, the engine computes statistical cluster variances in log-space, successfully isolating intermediate logical reasoning spikes and elevating them to stable non-linear 4-bit (
IQ4_XS) formats, while compressing idle fact-storage layers to aggressive 2-bit baselines. - Fading Boundary Tapering: Recognizes the extreme fragility of initial token entry data vectors, forcing an input wave cushion (
L00=IQ4_NL→L01=IQ4_XS→L02=IQ3_XXS) that gradually stabilizes parameters before hitting the fallback pools. - Dedicated Gate Insulation: Hard-shields volatile parallel Transformer Multi-Head Attention and Mamba Linear State Space Model (SSM) routing paths, keeping context tracking perfectly noise-free.
- Asymmetric Vocabulary Shielding: Fixes tied-weight boundary errors by mapping the final logit classification exit heads to robust configurations to completely eliminate formatting loops and API tag leakage under deep contexts.
- Native Next-N Speculative Stripping: Processed with advanced pre-tokenizer stripping to ensure zero index offset drift or layer-shifting risks across hybrid configurations.
🚀 Recommended Runtime Parameters (llama.cpp / llama-server)
Need to scale up? Deploy this exact script on on-demand cloud GPUs via RunPod Cloud Compute.
$./llama-server -m models/Qwen3.6-27B-YMQ-M-MTP.gguf -ctk q8_0 -ctv q4_0 --ctx-size 245760 --mmproj proj/Qwen3.6-27B-Q8.mmproj \
--spec-type draft-mtp --spec-draft-n-max 2 --timeout 36000 --checkpoint-min-step 2048 --ctx-checkpoints 4 \
--n-predict -1 --temp 0.6 --top-p 0.95 --top-k 20 --repeat-penalty 1.05 --jinja -fa
☕ Support & Future R&D
If the YMQ-Compiler builds saved your context window from collapsing or optimized your active development cycle speeds, consider buying a coffee to fund further low-level optimization research. Your support keeps the server nodes baking future model scales!
👉 Support ZeroDigest Research on ko-fi
📦 Source Framework & Automation Code
The compiler pipeline automation engine, setup thresholds, and structural mapping rules are open-source. To view the implementation details or compile your own custom models natively using this profile layout, visit the official development hub: