--- title: Qwen3.6-27B-YMQ-MTP-GGUF canonical_url: "https://www.modelscope.ai/models/Zero-Digest/Qwen3.6-27B-YMQ-MTP-GGUF" md_url: "https://www.modelscope.ai/models/Zero-Digest/Qwen3.6-27B-YMQ-MTP-GGUF.md" repository: Zero-Digest/Qwen3.6-27B-YMQ-MTP-GGUF last_updated: 2026-09-06 license: apache-2.0 pipeline_tag: text-generation tasks: - text-generation base_model: - Qwen/Qwen3.6-27B base_model_relation: quantized library_name: - gguf - pytorch frameworks: - pytorch downloads: 93 stars: 0 tags: - text-generation - gguf - quantizer - autoround - architecture-aware - mamba - ssm - multi-token-prediction - mtp --- # Qwen3.6-27B-YMQ-MTP-GGUF > Qwen3.6-27B-YMQ-MTP-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 sweeps to protect… Zero-Digest/Qwen3.6-27B-YMQ-MTP-GGUF is a text-generation model on ModelScope. licensed under apache-2.0. derived from Qwen/Qwen3.6-27B. - **Repository**: Zero-Digest/Qwen3.6-27B-YMQ-MTP-GGUF - **License**: apache-2.0 - **Tasks**: text-generation - **Base model**: Qwen/Qwen3.6-27B - **Tags**: text-generation, gguf, quantizer, autoround, architecture-aware, mamba, ssm, multi-token-prediction, mtp - **Downloads**: 93 - **Stars**: 0 - **Last updated**: 2026-09-06 Source: https://www.modelscope.ai/models/Zero-Digest/Qwen3.6-27B-YMQ-MTP-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)

🧡 BTC: 14Fmic9z3VA1ZU11bWoP6JtU7csTAApwZo

🔷 ETH/USDT: 0xbe4cdc3adc27c21ef1c27c6b403311db07b35ed2

# Qwen3.6-27B-YMQ-MTP-GGUF > **Source Model:** [Qwen/Qwen3.6-27B](https://huggingface.co/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: 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/Qwen3.6-27B-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](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)**