⚡ Fuel the Lab: Keep the Optimization Loops Running
Every single ZeroDigest YMQ-MTP release is handcrafted and manually calibrated via intensive importance-matrix sweeps to protect critical logic pathways. This project is entirely independent research—no automation bots, no corporate backers, and no external funding. Running multi-hour compute arrays consumes massive local infrastructure overhead out-of-pocket. Consider checking out our compiler or supporting our compute costs!
⚖️ An Architecture-Aware, AutoRound-Inspired Mixed Precision Layout
This repository features advanced, custom architecture-aware quantizations of Qwen3.8-27B Cold-Fusion GAIN V1.1 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).
ZeroDigest YMQ Logo
📊 Quantization Preset Tier Details
Preset Tier
Total Size
Target Usage / VRAM Profile
Cognitive Real-World Coding Quality
XXS-Pro
~9.1 GB
⚠️ Experimental Low-VRAM Sandbox
The Ultra-Compact Frontier.
XS-TI
~9.8 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.
XS-Pro
~10.6 GB
⚡ Dedicated 12GB VRAM Champion
The 12GB Efficiency Miracle. Features a highly optimized IQ4_XS/IQ3_XS gradient that keeps the model running fully inside VRAM with a clean 1.6GB context headroom buffer.
S-Pro
~12.5 GB
💎 Premium 16GB Workstation Driver
The Efficiency Miracle. Holds an elite 6.50 PPL for pristine conversational fluidness while using full Q5_K attention armor.
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
🎯 High-Context Agent Workspace (Recommended)
The Flagship Masterpiece. Heavy structural shielding for maximum logical durability across massive enterprise repositories.
L-TI
~14.1 GB
🔥 Heavy 16GB Workstation Choice (Precision)
The 16GB High-Fidelity Champion. Features a robust Q6_K/IQ4_NL armored peak [local]. Minimizes information entropy drift to keep structural logic stable over long, multi-turn agent execution loops.
🎯 Quick Selection Guide
Use Case
Recommended Preset
Why
12GB GPU
XS-TI or XS-Pro
Best 12GB card performance with high-context support
16GB GPU (Recommended)
M-TI or S-Pro
The sweet spot for coding tasks with optimal VRAM headroom
24GB GPU
L-TI or M
~14GB models fit well on 24GB VRAM with headroom for context
Low-VRAM / Experimental
XXS-Pro
Sub-10GB footprint for testing and lightweight workflows
📉 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.
💡 The Multi-Tier Grid Breakthrough: Standard S vs. S-Pro
During intensive local workspace validation passes, our architecture-aware YMQ-Compiler successfully mapped out a radical new bit-allocation matrix. By splitting the layer distribution, we created a premium, high-fidelity alternative to our standard budget tier:
S-Pro Preset (~12.5 GB): Perplexity = 6.5087. By aggressively compressing auxiliary tensor lanes down to Q2_K but raising the background baseline floor to IQ3_XXS, S-Pro eliminates a massive wave of background quantization noise while only adding a few megabytes of file weight.
The practical result is a premium, low-overhead everyday driver for 16GB GPU setups. Backed by full Q5_K reasoning armor.
⚖️ 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.
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!
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:
