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staccs/Qwen3-32B-lecore-19g-GGUF overview

Qwen3 32B lecore 19g GGUF GGUF builds of Qwen/Qwen3 32B https://huggingface.co/Qwen/Qwen3 32B for llama.cpp, Ollama and LM Studio. These are companions to the …

ggufllama.cppollamalm-studioquantizedlecoretext-generationbase_model:Qwen/Qwen3-32Bbase_model:quantized:Qwen/Qwen3-32Bendpoints_compatibleregion:usconversational

Runs locally from ~17.36 GB disk (24 GB VRAM class GPUs with llama.cpp / guIDE).

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text-generation
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Repository Files & Downloads

2 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
Qwen3-32B-lecore-19g-GGUF-Q4_K_M.ggufGGUFQ4_K_M18.40 GBDownload
Qwen3-32B-lecore-19g-GGUF-lecore-mix.ggufGGUFGGUF17.36 GBDownload

Model Details

Model IDstaccs/Qwen3-32B-lecore-19g-GGUF
Authorstaccs
Pipelinetext-generation
License—
Base modelQwen/Qwen3-32B
Last modified2026-10-15T18:26:37.000Z

Model README

---

base_model: Qwen/Qwen3-32B

library_name: gguf

pipeline_tag: text-generation

tags: [gguf, llama.cpp, ollama, lm-studio, quantized, lecore]

---

Qwen3-32B-lecore-19g-GGUF

GGUF builds of Qwen/Qwen3-32B for llama.cpp, Ollama and LM Studio.

These are companions to the MLX export staccs/Qwen3-32B-lecore-19g-mlx, which has the measured perplexity numbers.

| File | Size | What it is |

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

| Qwen3-32B-lecore-19g-GGUF-Q4_K_M.gguf | 19.76 GB | stock llama.cpp Q4_K_M |

| Qwen3-32B-lecore-19g-GGUF-lecore-mix.gguf | 18.64 GB | leCore per-tensor mix: each linear layer gets the GGUF K-quant matching the bits leCore measured for it (2→Q2_K, 3→Q3_K, 4→Q4_K, 8→Q8_0); embeddings and output follow the MLX export |

The lecore-mix file is quantized from the original bf16 weights with llama.cpp K-quants. The bit width of each tensor follows leCore's measured (KL-damage) allocation, so its numbers are close to, but not identical to, the MLX export.

Some tensor widths are not multiples of 256, so llama.cpp may fall back to a compatible type for those tensors.

Note: at this budget every linear tensor was allocated 4 bits, so lecore-mix is in practice a uniform Q4_K build (with a Q6_K output layer). GSM8K regressed in the MLX export. See the MLX card for the diagnosis.

Usage

Ollama: download a file, then

printf 'FROM ./Qwen3-32B-lecore-19g-GGUF-Q4_K_M.gguf
' > Modelfile && ollama create qwen3-32b-lecore-19g-gguf -f Modelfile && ollama run qwen3-32b-lecore-19g-gguf

or pull straight from the Hub with ollama run hf.co/staccs/Qwen3-32B-lecore-19g-GGUF:Q4_K_M

LM Studio: search for staccs/Qwen3-32B-lecore-19g-GGUF in the model browser and pick a file. On a 36 GB Mac, the lecore-mix file is the one that fits.

llama.cpp

llama-cli -hf staccs/Qwen3-32B-lecore-19g-GGUF -p "Hello" -n 256

Benchmarks (lm-eval-harness 0.4.9, run on the shipped weights dequantized to bf16 on GPU)

MMLU 5-shot on the first 10 questions of each of the 57 subjects; GSM8K 5-shot on the first 100 test problems (greedy, 320 new tokens, no chat template). These are subsets, so expect about ±2–4 points of sampling noise.

| Model | MMLU (5-shot) | GSM8K strict | GSM8K flexible |

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

| leCore MLX export (numbers measured on the MLX weights; these GGUF files were not benchmarked) | 85.3% | 27.0% | 34.0% |

| stock 3-bit affine, group 64 (simulated) | 80.9% | 83.0% | 78.0% |

License

Base model: Qwen/Qwen3-32B, released by the Qwen team under the Apache License 2.0 (https://huggingface.co/Qwen/Qwen3-32B/blob/main/LICENSE). Only quantization was done here: no retraining.

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