Qwen3.8-27B-YMQ-MTP-GGUF
Source Model: Qwen/Qwen3.8-27B
⚖️ An Architecture-Aware, AutoRound-Inspired Mixed Precision Layout
This repository features advanced, custom architecture-aware quantizations of Qwen3.8-27B 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-TI | ~10.2 GB | ⚡ 12GB Card Lifeline (High-Context) | The 12GB Context Champion. Safely pins core attention layers to a stable IQ3_XXS gradient while crushing non-critical auxiliary arrays to IQ2_XS. |
M-TI | ~12.6 GB | 💎 Premium 16GB GPU Workspace Driver | The 16GB Workstation Choice. Employs a robust Q5_K/IQ4_XS mixed-precision gradient that protects logical reasoning focus while leaving over 3.4GB of VRAM wide open. |
M | ~14.5 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 metrics demonstrate the mathematical quality preservation of the YMQ-Compiler log-space cluster analysis compared to standard linear quantization layouts. Tested natively via llama-perplexity over a 4096 context window using the official WikiText-2 test corpus.
| Model Variant | File Size | Perplexity | Mean KL-Divergence | Internal Bit Gradient (High ➔ Mid ➔ Low ➔ Default ➔ Floor) |
|---|---|---|---|---|
XXS | ~9.8 GB | 7.7848 | 0.193804 ± 0.0022 | IQ3_XXS ➔ IQ2_S ➔ IQ2_XS ➔ IQ2_XS (No Floor) |
XS-TI | ~10.2 GB | 7.6090 | 0.168378 ± 0.001935 | IQ3_XXS ➔ IQ3_XXS ➔ IQ3_XXS ➔ IQ3_XXS ➔ IQ2_XXS |
M-TI | ~12.9 GB | 7.0109 | 0.104087 ± 0.0016 | Q5_K ➔ IQ4_XS ➔ IQ3_S ➔ IQ3_XXS ➔ IQ2_XXS |
M | ~14.5 GB | 6.8413 | 0.053286 ± 0.0013 | Q5_K ➔ IQ4_XS ➔ IQ3_S ➔ IQ3_XXS (No Floor) |
L | ~17.0 GB | 6.9791 | 0.031546 ± 0.0009 | Q6_K ➔ Q5_K ➔ IQ4_NL ➔ IQ3_S (No Floor) |
XL | ~19.0 GB | 6.8196 | 0.011598 ± 0.0005 | Q6_K ➔ Q6_K ➔ Q5_K ➔ IQ4_NL (No Floor) |
💡 The Core Architectural Discovery
Notice the dramatic performance leap between the S and M presets. The YMQ-Compiler log-space algorithm automatically detects the true data signals on the newly updated Qwen 3.8 hybrid Attention/Mamba routing nodes.
By shifting the quantization boundaries slightly in the M preset, the engine safely promotes the model's high-leverage logical spikes straight into full high-fidelity precision layers. This drops the perplexity score down to an elite 6.8413—matching the raw reasoning power of the massive 19GB XL file while clawing back a clean 5 Gigabytes of VRAM overhead cache space for your local agent environments!
⚖️ 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/Qwen3.8-27B-YMQ-M.gguf -ctk q8_0 -ctv q4_0 --ctx-size 245760 --mmproj models/Qwen3.8-27B.mmproj-Q6_K.gguf \
--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
🖼️ Vision Projection (--mmproj)
For multimodal vision support, pair these builds with one of the following projection files:
| Variant | File | Size | Notes |
|---|---|---|---|
| Full Precision (F16) | Qwen3.8-27B.mmproj-f16.gguf | ~928 GB | Full-precision native vision tower. Maximum fidelity for image reasoning tasks. |
| Q6_K | Qwen3.8-27B.mmproj-Q6_K.gguf (this repo) | ~587 MB | High-fidelity quantized vision tower. Excellent quality-to-size balance with minimal perceptible degradation. |
| Q4_K_S | Qwen3.8-27B.mmproj-Q4_K_S.gguf (this repo) | ~478 MB | Compact vision projection for VRAM-constrained setups. Retains strong image understanding at reduced footprint. |
Pass via --mmproj <path-to-file> in your llama-server invocation (see example above).
☕ 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: