Qwen3.8-27B-GSQ-RCO-GGUF

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Qwen3.8-27B · GSQ-RCO GGUFs

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

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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.

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

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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.

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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.

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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.

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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 -->

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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.

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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:

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.

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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}
}

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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.

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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.

<div align="center"> _{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} </div>