---

base_model: google/gemma-3-27b-it

library_name: gguf

pipeline_tag: text-generation

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

---

gemma-3-27b-it-lecore-GGUF

GGUF builds of google/gemma-3-27b-it for llama.cpp, Ollama and LM Studio.

These are companions to the MLX export staccs/gemma-3-27b-it-lecore-mlx, which has the measured perplexity numbers.

| File | Size | What it is |

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

| gemma-3-27b-it-Q4_K_M.gguf | 16.55 GB | stock llama.cpp Q4_K_M |

| gemma-3-27b-it-lecore-mix.gguf | 11.69 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.

Usage

Ollama: download a file, then

printf 'FROM ./gemma-3-27b-it-Q4_K_M.gguf
' > Modelfile && ollama create gemma-3-27b-it-lecore-gguf -f Modelfile && ollama run gemma-3-27b-it-lecore-gguf

or pull straight from the Hub with ollama run hf.co/staccs/gemma-3-27b-it-lecore-GGUF:Q4_K_M

LM Studio: search for staccs/gemma-3-27b-it-lecore-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/gemma-3-27b-it-lecore-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 mix (this repo) | 75.1% | 89.0% | 89.0% |

| stock 3-bit affine, group 64 (simulated) | 75.3% | 68.0% | 74.0% |

License

Base model: google/gemma-3-27b-it (weights taken from the identical public mirror unsloth/gemma-3-27b-it). Gemma is provided under and subject to the Gemma Terms of Use (https://ai.google.dev/gemma/terms) and its Prohibited Use Policy (https://ai.google.dev/gemma/prohibited_use_policy). This export is text-only: the vision tower was dropped. Only quantization was done here: no retraining.