--- title: Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-YMQ-GGUF canonical_url: "https://www.modelscope.ai/models/Zero-Digest/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-YMQ-GGUF" md_url: "https://www.modelscope.ai/models/Zero-Digest/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-YMQ-GGUF.md" repository: Zero-Digest/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-YMQ-GGUF last_updated: 2026-09-04 license: apache-2.0 pipeline_tag: text-generation tasks: - text-generation base_model: - DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1 base_model_relation: quantized library_name: - gguf - pytorch frameworks: - pytorch downloads: 107 stars: 0 tags: - text-generation - gguf - quantizer - autoround - architecture-aware - mamba - ssm - multi-token-prediction - mtp - 27b --- # Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-YMQ-GGUF > Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-YMQ-GGUF - An open-source model by Zero-Digest on ModelScope. ⚡ Fuel the Lab: Keep the Optimization Loops Running Every single ZeroDigest YMQ-MTP release is handcrafted and manually calibrated via intensive importance-matrix… Zero-Digest/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-YMQ-GGUF is a text-generation model on ModelScope. licensed under apache-2.0. derived from DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1. - **Repository**: Zero-Digest/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-YMQ-GGUF - **License**: apache-2.0 - **Tasks**: text-generation - **Base model**: DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1 - **Tags**: text-generation, gguf, quantizer, autoround, architecture-aware, mamba, ssm, multi-token-prediction, mtp, 27b - **Downloads**: 107 - **Stars**: 0 - **Last updated**: 2026-09-04 Source: https://www.modelscope.ai/models/Zero-Digest/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-YMQ-GGUF ---

⚡ 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!

👉 Developer Resources & Support Paths:

🛠️ View Compiler Source on GitHub (YMQ v2.0)

🚀 Deploy weights on RunPod Cloud Compute (Affiliate Link)

☕ Support the Lab on Ko-fi (One-Time / Monthly)

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# Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-YMQ-GGUF > **Source Model:** [DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1](https://huggingface.co/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 | 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. | Model Variant | File Size | Perplexity | Mean KL-Divergence | Internal Bit Gradient (High ➔ Mid ➔ Low ➔ Default ➔ Floor) | | :--- | :--- | :--- | :--- | :--- | | **`XXS-Pro`** | [~9.1 GB](./Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-YMQ-XXS-Pro.gguf) | 7.2336 | `0.185361 ± 0.0021` | `IQ3_XXS` ➔ `IQ3_XXS` ➔ `IQ2_S` ➔ `IQ2_S` ➔ `IQ2_XXS` | | **`XS-TI`** | [~9.8 GB](./Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-YMQ-XS-TI.gguf) | 7.0068 | `0.168378 ± 0.001935` | `IQ3_XXS` ➔ `IQ3_XXS` ➔ `IQ3_XXS` ➔ `IQ3_XXS` ➔ `IQ2_XXS` | | **`XS-Pro`** | [~10.6 GB](./Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-YMQ-XS-Pro.gguf) | 6.7987 | `0.135866 ± 0.0018` | `IQ4_XS` ➔ `IQ3_XXS` ➔ `IQ3_XXS` ➔ `IQ3_XXS` ➔ `IQ2_XXS` | | **`S-Pro`** | [~12.5 GB](./Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-YMQ-S-Pro.gguf) | 6.5087 | **`0.080403 ± 0.0014`** | `Q5_K` ➔ `IQ3_S` ➔ `IQ3_XXS` ➔ `IQ3_XXS` *(No Floor)* | | **`M-TI`** | [~12.9 GB](./Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-YMQ-M-TI.gguf) | **6.6290** | `0.104087 ± 0.0016` | `Q5_K` ➔ `IQ4_XS` ➔ `IQ3_S` ➔ `IQ3_XXS` ➔ `IQ2_XXS` | | **`M`** | [~14.5 GB](./Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-YMQ-M.gguf) | **6.3663** | `0.053286 ± 0.0013` | `Q5_K` ➔ `IQ4_XS` ➔ `IQ3_S` ➔ `IQ3_XXS` *(No Floor)* | | **`L-TI`** | [~14.1 GB](./Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-YMQ-L-TI.gguf) | **6.5786** | **`0.086571 ± 0.0013`** | `Q6_K` ➔ `IQ4_NL` ➔ `IQ4_XS` ➔ `IQ3_S` ➔ `IQ2_XS` | ### 💡 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](https://runpod.io?ref=dbjmkmeh).* ``` $./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: | Variant | File | Size | Notes | | :--- | :--- | :--- | :--- | | **Full Precision (F16)** | [mmproj-F16.gguf](https://huggingface.co/DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-NM-DAU-NEO-MAX-MTP-GGUF/blob/main/mmproj-F16.gguf) | ~928 MB | Full-precision vision tower. Maximum fidelity for image reasoning tasks. | | **Q6_K** | [mmproj-Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-Q6_K.gguf](./mmproj-Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-Q6_K.gguf) (this repo) | ~587 MB | High-fidelity quantized vision tower. Excellent quality-to-size balance with minimal perceptible degradation. | | **Q4_K_S** | [mmproj-Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-Q4_K_S.gguf](./mmproj-Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-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 ` 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](https://ko-fi.com/zerodigest)** --- ## 📦 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](https://github.com/minyor/ymq-compiler)**