---

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

- mixed-precision

- ist-daslab

- multimodal

- vision

---

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GSQ-RCO GGUF release card, TEMPLATE (filled with Qwen3.8-27B as the example).

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<a href="https://github.com/IST-DASLab"><img src="https://huggingface.co/ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF/resolve/main/assets/banner.png" alt="GGUF, GSQ-RCO dynamic non-uniform quantization" width="100%"/></a>

<br/>

Qwen3.8-27B &middot; GSQ-RCO GGUFs

Non-uniform GGUF quantizations produced with GSQ and RCO, with a vision projector for multimodal use.







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!Task average vs bit-width

!Speculative decoding with the MTP head

---

Overview

This repository provides GGUF quantizations of Qwen3.8-27B at four sizes, together with the model's vision projector (mmproj) for multimodal use. In contrast to uniform quantization, which applies a single quantization type to all weight tensors, each model here assigns a separate quantization type to every tensor. The assignment is obtained by a gradient-based search that allocates precision according to per-tensor sensitivity, subject to a total size budget. The resulting files are standard GGUF and run unmodified in llama.cpp, Ollama, and LM Studio.

> Method summary. GSQ provides accurate low-bit scalar quantization of each tensor at a given quantization type; RCO assigns the per-tensor quantization types under a size budget. Together they yield a non-uniform GGUF at the requested size.

| Method | Description |

|---|---|

| GSQ (Gumbel-Softmax Quantization, paper, code) | Post-training scalar quantization that jointly learns the per-coordinate grid assignments and the per-group scales via a Gumbel-Softmax relaxation. GSQ closes most of the gap between scalar and vector quantization at 2 to 3 bits while remaining deployable in standard scalar formats such as GGUF. |

| RCO (Riemannian Constrained Optimization, paper, code) | Assigns one of K quantization types to each of N tensors under a total size budget. The budget constraint is reformulated as a smooth Riemannian manifold in logit space, which permits gradient-based optimization directly on the task loss while enforcing the budget exactly, without constraint-specific hyperparameter tuning. |

Both methods were developed at the Deep Algorithms and Systems Lab (DASLab), Institute of Science and Technology Austria.

---

Available files

Files follow the convention <model>-GSQ-RCO-<type>.gguf, where the suffix names the quantization class; the table lists each file's true whole-file average bit-width. The mmproj file carries the vision encoder and projector at BF16; one copy serves all quantizations.

<!-- [swap] rows below (filenames, bpw, size, notes). -->

| File | bpw | Size | Notes |

|---|---|---|---|

| Qwen3.8-27B-GSQ-RCO-IQ2_XS.gguf | 2.50 | 8.4 GB | Smallest; zero-shot above the BF16 baseline |

| Qwen3.8-27B-GSQ-RCO-IQ2_S.gguf | 2.75 | 9.3 GB | Matches the base model on AIME25 |

| Qwen3.8-27B-GSQ-RCO-IQ3_XXS.gguf | 3.00 | 10.1 GB | Strong all-round operating point |

| Qwen3.8-27B-GSQ-RCO-IQ3_S.gguf | 3.50 | 11.8 GB | Recommended; task-lossless |

| mmproj-Qwen3.8-27B-BF16.gguf | 16 | 0.9 GB | Vision encoder + projector, for multimodal use |

Each quantization also ships an optional -mtp build (about 0.35 GB larger) that carries the model's Multi-Token Prediction head for speculative decoding in llama.cpp. The weights are otherwise identical, so quality is unchanged.

The IQ3_S model is the task-lossless operating point: it matches the base model exactly on AIME25 (100.00) and LiveCodeBench v6 (85.71) and is within 0.51 points on GPQA-Diamond, at just over one fifth of the BF16 size.

---

Results

All models are evaluated against the BF16 base model and the Unsloth Dynamic (UD) quantizations of the same base model. We report perplexity on wikitext2, C4, and FineWeb-Edu, the average over five zero-shot tasks (arc_easy, arc_challenge, hellaswag, winogrande, piqa), recovery (zero-shot average relative to BF16), and three reasoning and generation benchmarks: AIME25, GPQA-Diamond, and LiveCodeBench v6. Sizes are those of the files as evaluated.

<!-- [swap] paste the Markdown table printed by tools/make_plots.py -->

| Variant | bpw | GB | wiki↓ | c4↓ | fw↓ | ZS avg↑ | recovery | AIME25↑ | GPQA-D↑ | LCB v6↑ |

|---|---|---|---|---|---|---|---|---|---|---|

| BF16 | 16.00 | 53.8 | 7.05 | 11.45 | 8.14 | 74.34 | 100.0% | 100.00 | 89.90 | 85.71 |

| GSQ-RCO IQ2_XS | 2.50 | 8.4 | 7.69 | 12.98 | 9.19 | 74.54 | 100.3% | 96.67 | 84.85 | 76.57 |

| GSQ-RCO IQ2_S | 2.75 | 9.3 | 7.39 | 12.40 | 8.80 | 75.70 | 101.8% | 100.00 | 86.36 | 82.29 |

| GSQ-RCO IQ3_XXS | 3.00 | 10.1 | 7.20 | 12.13 | 8.59 | 74.81 | 100.6% | 100.00 | 88.89 | 84.57 |

| GSQ-RCO IQ3_S | 3.50 | 11.8 | 7.07 | 11.76 | 8.34 | 74.47 | 100.2% | 100.00 | 89.39 | 85.71 |

| UD-IQ2_S | 2.49 | 8.4 | 8.02 | 12.78 | 9.08 | 73.80 | 99.3% | 86.67 | 76.26 | 72.00 |

| UD-Q2_K_XL | 2.88 | 9.8 | 7.54 | 12.25 | 8.69 | 74.37 | 100.0% | 100.00 | 86.87 | 82.28 |

| UD-IQ3_S | 3.52 | 12.0 | 7.16 | 11.75 | 8.34 | 75.49 | 101.5% | 96.67 | 89.90 | 84.00 |

At 3.50 bpw, IQ3_S is task-lossless: it reproduces the base model exactly on AIME25 (100.00) and LiveCodeBench v6 (85.71) and trails it by 0.51 points on GPQA-Diamond, giving a task average of 91.70 against the base model's 91.87 (99.8%) at 11.8 GB, a 4.6x size reduction. Against UD-IQ3_S it leads by 3.33 points on AIME25 and 1.71 on LiveCodeBench while being 0.2 GB smaller, though UD holds GPQA-Diamond by 0.51. At 3.00 bpw the model already matches the base on AIME25 at 10.1 GB, and at matched file size (8.4 GB) IQ2_XS leads UD-IQ2_S by 10.00 points on AIME25, 8.59 on GPQA-Diamond, and 4.57 on LiveCodeBench v6. <!-- [swap] one-line observation -->

!AIME25 vs bit-width

!GPQA-Diamond vs bit-width

!LiveCodeBench v6 vs bit-width

---

Usage

llama.cpp

# download (requires: pip install -U "huggingface_hub[cli]")
hf download ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF Qwen3.8-27B-GSQ-RCO-IQ3_XXS.gguf --local-dir .

llama-cli -m Qwen3.8-27B-GSQ-RCO-IQ3_XXS.gguf -p "Explain mixed-precision quantization." -ngl 99

Vision (multimodal)

hf download ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF mmproj-Qwen3.8-27B-BF16.gguf --local-dir .

llama-mtmd-cli -m Qwen3.8-27B-GSQ-RCO-IQ3_XXS.gguf \
  --mmproj mmproj-Qwen3.8-27B-BF16.gguf \
  --image photo.jpg -p "Describe this image."

The projector was converted directly from the base checkpoint and verified against these quantizations.

Ollama

ollama run hf.co/ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF   # pick the file matching your memory budget

LM Studio

Search the repo name, then pick a GSQ-RCO-* build from the file list.

---

Quantization procedure

1. Per-tensor database. Each weight tensor is quantized at every candidate GGUF quantization type with GSQ, yielding a searchable database of quantized tensor variants.
2. RCO search. The budget-constrained Riemannian search assigns one quantization type per tensor such that the whole-file average bit-width meets the target.
3. Assembly. The selected per-tensor variants are stitched into a single standard GGUF file.

Reference implementations: GSQ at IST-DASLab/GSQ and RCO at IST-DASLab/RCO.

Reproducibility artifacts

Each released GGUF ships the files needed to audit how it was built:

| File | Contents |

|---|---|

| tensor-allocation/<model>.rco-allocation.txt | The quantization type assigned to every tensor in that file, with a quant-type histogram and the target bit-width. This is the RCO search result, so the allocation can be inspected without opening the model. |

| imatrix-qwen3.8-27b.gguf | The importance matrix used during quantization (1000 chunks of 4096 tokens). |

The -mtp builds have their own allocation dumps; they list the same per-tensor assignment as the base model plus the 15 tensors of the MTP head.

---

Citation

If you use these models or methods, please cite both papers:

@article{gsq2026,
  title  = {GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling},
  author = {Dadgarnia, Alireza and Tabesh, Soroush and Nikdan, Mahdi and Helcig, Michael and Kurtic, Eldar and Kleinegger, Maximilian and Alistarh, Dan},
  journal= {arXiv preprint arXiv:2604.18556},
  year   = {2026}
}
@article{rco2026,
  title  = {Model Compression with Exact Budget Constraints via Riemannian Manifolds},
  author = {Helcig, Michael and Alistarh, Dan},
  journal= {arXiv preprint arXiv:2605.00649},
  year   = {2026}
}

---

Acknowledgements

We thank Verda and Scientific Computing at the Institute of Science and Technology Austria for providing the compute resources used to produce these models.

---

License

These quantized weights inherit the license of the base model (Qwen3.8-27B). The GSQ-RCO tooling is released by the Deep Algorithms and Systems Lab under its repository license.

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_{Built with <b>GSQ</b> and <b>RCO</b> at the <a href="https://github.com/IST-DASLab">Deep Algorithms and Systems Lab</a> &middot; Institute of Science and Technology Austria}

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