Qwen3-8B — GGUF K-Quant refined with GSQ

GGUF K-Quant checkpoints of Qwen/Qwen3-8B

in which the discrete grid assignments have been refined with GSQ

(Gumbel-Softmax Quantization), starting from the public Unsloth GGUF

initialization and projected back into the same K-Quant format. The

optimized files run unchanged on llama.cpp / Ollama.

The gains are largest in the aggressive Q2_K setting:

| Quant | Init (Unsloth) avg | GSQ avg |

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

| Q2_K | 50.03 | 56.28 |

(See the GSQ paper / GitHub repo for the full per-benchmark breakdown.)

• Paper: GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling (arXiv:2604.18556)
• Paper page on HF:
• Code:
• Collection:

Quantization details

• Base model: Qwen/Qwen3-8B
• Format: GGUF K-Quant (drop-in replacement for the Unsloth files)
• Pipeline: Unsloth GGUF init → GSQ Gumbel-Softmax refinement → re-pack into K-Quant
• Runtime: llama.cpp, ollama, LM Studio, anything that consumes GGUF

Storage layout

These files are bit-for-bit standard GGUF K-Quant. GSQ only changes the

values of the quantized weights (it relearns the discrete grid assignments

inside each K-Quant block), not the block structure, scales, or super-block

layout. As a result:

• The file size matches the corresponding upstream K-Quant for the same quant tier.
• Any llama.cpp / ollama build that loads regular Qwen3-8B-Q2_K.gguf

loads this file with zero changes.

• The Hugging Face UI reports GGUF block types (e.g. Q2_K, Q4_K,

Q6_K) rather than per-tensor dtypes — those refer to the on-disk K-Quant

encoding, not the precision of any optimizer state.

Usage with llama.cpp

# Download a specific quant from this repo
huggingface-cli download ISTA-DASLab/Qwen3-8B-GGUF-GSQ \
  Qwen3-8B-Q2_K.gguf --local-dir .

# Run with llama.cpp
./llama-cli -m Qwen3-8B-Q2_K.gguf -p "Hello"

Usage with Ollama

ollama run hf.co/ISTA-DASLab/Qwen3-8B-GGUF-GSQ:Q2_K

Citation

@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 Kurti{\'c}, Eldar and Kleinegger, Max and Alistarh, Dan},
  journal= {arXiv preprint arXiv:2604.18556},
  year   = {2026},
  url    = {https://arxiv.org/abs/2604.18556}
}