Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-YMQ-GGUF

Source Model: DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1

⚖️ 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).

📊 Quantization Preset Tier Details

🎯 Quick Selection Guide

📉 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:

1. 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.
2. 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.
3. 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.
4. 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.
5. 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-Cold-Fusion-GAIN-V1.1-YMQ-M.gguf -ctk q8_0 -ctv q4_0 --ctx-size 245760 --mmproj models/mmproj-Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-Q6_K.gguf \
  --spec-type draft-mtp --spec-draft-n-max 3 --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:

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:

👉 GitHub: ZeroDigest / YMQ-Compiler