ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF — new model trending #3 on Hugging Face

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

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

[](https://arxiv.org/abs/2604.18556)

[](https://arxiv.org/abs/2605.00649)

[](https://github.com/IST-DASLab/GSQ)

[](https://github.com/IST-DASLab/RCO)

[](https://github.com/IST-DASLab)

[](#license)

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---

## 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](https://arxiv.org/abs/2604.18556), [code](https://github.com/IST-DASLab/GSQ)) | 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](https://arxiv.org/abs/2605.00649), [code](https://github.com/IST-DASLab/RCO)) | 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)](https://github.com/IST-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 -->




---

## Usage

### llama.cpp

```bash

# 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)

```bash

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

```bash

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](https://github.com/IST-DASLab/GSQ) and **RCO** at [IST-DASLab/RCO](https://github.com/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:

```bibtex

@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](https://verda.com/) and Scientific Computing at the Institute of Science and Technology Austria for providing the compute resources used to produce these models.

Text extracted automatically; images, tables and formatting may be missing. Original: https://huggingface.co/ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF