---

base_model: Qwen/Qwen3.8-27B

base_model_relation: quantized

pipeline_tag: image-text-to-text

library_name: gguf

license: apache-2.0

tags:

- gguf

- gsq

- rco

- quantization

- multimodal

- vision

- buffed-mod

---

Qwen3.8-27B-GSQ-RCO — BuffedMod GGUF

This repository contains modified versions of the Qwen3.8-27B-GSQ-RCO-IQ3_S GGUF model.

Unlike traditional re-quantization workflows which require quantizing the entire model from unquantized FP16/BF16 weights, these files were created by directly modifying specific tensor weights within the binary GGUF file.

By replacing critical layer tensors (specifically output.weight) with higher-precision quantizations, we achieve better Perplexity (PPL), lower KL Divergence (KLD), and increased top-token agreement while maintaining a compact footprint.

---

⚡ What Changed in BuffedMod?

In lower-bit quantizations like IQ3_S, the output.weight tensor (LM Head) often serves as a major bottleneck for final text generation precision. Upgrading this tensor improves top-token probability matching without adding massive overhead to the overall file size.

📦 Available Variants

1. Qwen3.8-27B-GSQ-RCO-BuffedMod-IQ3_S_XL-mtp-Q6K.gguf

* Modifications: Replaced output.weight from Q4_K to Q6_K.

* Target: Maximum quality retention for IQ3_S baseline.

1. Qwen3.8-27B-GSQ-RCO-BuffedMod-IQ3_S_XL-mtp-IQ4XS.gguf

* Modifications: Replaced output.weight from Q4_K to Q6_K AND down-quantized MTP (Multi-Token Prediction) tensors from Q6_K to IQ4_XS.

* Target: Optimized size-to-performance balance by reclaiming memory from non-essential auxiliary tensors.

---

📊 Benchmark & Evaluation Results

All variants were evaluated using llama-perplexity with original BF16 base model (q38f16baseline.kld) to measure Perplexity (PPL) degradation, KL Divergence (KLD), and Token Probability shifts.

llama-perplexity.exe -f /wikitext-2-raw/wiki.test.raw \
    --kl-divergence \
    --kl-divergence-base q38f16baseline.kld \
    -ngl 99 \
    -m <model_variant>.gguf

📈 Comparison Summary

| Metric | Original (IQ3_S) | BuffedMod (Q6_K Output) | Improvement |

| :--- | :---: | :---: | :---: |

| Mean PPL(Q) | 7.062697 | 7.032142 | 🟢 -0.030555 (Lower is better) |

| Mean PPL Diff vs Base | 0.112205 | 0.081650 | 🟢 -27.2% Error Reduction |

| Mean KLD | 0.055475 | 0.051738 | 🟢 -6.7% Divergence |

| Median KLD | 0.022928 | 0.018899 | 🟢 -17.5% Divergence |

| Same Top Token Probability | 89.657% | 90.535% | 🟢 +0.878% Match |

| RMS Δp | 6.537% | 6.285% | 🟢 -0.252% Variance |

---

📐 Model Variant Comparison

The table below highlights the trade-off between file size efficiency and accuracy gains across the original and BuffedMod variants:

| Model Variant | File Size | PPL(Q) ↓ | PPL Diff vs Base ↓ | Mean KLD ↓ | Median KLD ↓ | Top-Token Match ↑ |

| :--- | :---: | :---: | :---: | :---: | :---: | :---: |

| Original (IQ3_S-mtp) | 11.29 GB | 7.0627 | 0.1122 | 0.0555 | 0.0229 | 89.66% |

| BuffedMod (IQ4XS MTP) | 11.49 GB | 7.0321 | 0.0817 | 0.0517 | 0.0189 | 90.54% |

| BuffedMod (Q6_K MTP) | 11.59 GB | 7.0321 | 0.0817 | 0.0517 | 0.0189 | 90.54% |

> 📌 Key Highlights:

> * BuffedMod (IQ4XS MTP) offers the optimal sweet spot: it gains the full accuracy boost (+0.88% top-token match, -27.2% PPL error vs base) with only +200 MB size overhead compared to the original file.

> * BuffedMod (Q6_K MTP) retains maximum fidelity on the auxiliary MTP tensors for an extra +100 MB, maintaining identical core perplexity and divergence performance.

📋 Full Metrics Breakdown

🔴 Baseline Original Metrics (IQ3_S with Q4_K output.weight)

====== Perplexity statistics ======
Mean PPL(Q)                   :   7.062697 ±   0.045484
Mean PPL(base)                :   6.950493 ±   0.044934
Cor(ln(PPL(Q)), ln(PPL(base))):  98.65%
Mean ln(PPL(Q)/PPL(base))     :   0.016014 ±   0.001059
Mean PPL(Q)/PPL(base)         :   1.016143 ±   0.001076
Mean PPL(Q)-PPL(base)         :   0.112205 ±   0.007438

====== KL divergence statistics ======
Mean    KLD:   0.055475 ±   0.000453
Maximum KLD:  15.811345
99.9%   KLD:   2.067696
99.0%   KLD:   0.593792
95.0%   KLD:   0.188752
90.0%   KLD:   0.110145
Median  KLD:   0.022928

====== Token probability statistics ======
Mean    Δp: -0.513 ± 0.017 %
RMS Δp    :  6.537 ± 0.051 %
Same top p: 89.657 ± 0.079 %

🟢 BuffedMod Metrics (IQ3_S with Q6_K output.weight)

====== Perplexity statistics ======
Mean PPL(Q)                   :   7.032142 ±   0.045271
Mean PPL(base)                :   6.950493 ±   0.044934
Cor(ln(PPL(Q)), ln(PPL(base))):  98.72%
Mean ln(PPL(Q)/PPL(base))     :   0.011679 ±   0.001034
Mean PPL(Q)/PPL(base)         :   1.011747 ±   0.001046
Mean PPL(Q)-PPL(base)         :   0.081650 ±   0.007236

====== KL divergence statistics ======
Mean    KLD:   0.051738 ±   0.000450
Maximum KLD:  15.754954
99.9%   KLD:   2.056773
99.0%   KLD:   0.586696
95.0%   KLD:   0.181908
90.0%   KLD:   0.103020
Median  KLD:   0.018899

====== Token probability statistics ======
Mean    Δp: -0.477 ± 0.016 %
RMS Δp    :  6.285 ± 0.052 %
Same top p: 90.535 ± 0.076 %

📜 Credits & Acknowledgments

• Original Model: ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF
• Modification: Tweak & Direct Tensor Modification by tooltd