gemma-2-27b-GGUF

tensorblock/gemma-2-27b-GGUF

gemma-2-27b-GGUF is a machine learning model from tensorblock. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as gemma.

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Downloads · 30 days

198

8% of all-time downloads

All-time downloads

2.4K

Public

Repo size

414 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.gguf23.9 GB · 100%

At a glance

Library: transformers
License: gemma
Access: Public
Created: Nov 21, 2024
Updated: Jan 27, 2026
SHA: 12bba5ba

Base models

unsloth/gemma-2-27b

Model card

From the Hugging Face model README

unsloth/gemma-2-27b - GGUF

This repo contains GGUF format model files for unsloth/gemma-2-27b.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

Our projects

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Model file specification

Filename	Quant type	File Size	Description
gemma-2-27b-Q2_K.gguf	Q2_K	10.450 GB	smallest, significant quality loss - not recommended for most purposes
gemma-2-27b-Q3_K_S.gguf	Q3_K_S	12.169 GB	very small, high quality loss
gemma-2-27b-Q3_K_M.gguf	Q3_K_M	13.425 GB	very small, high quality loss
gemma-2-27b-Q3_K_L.gguf	Q3_K_L	14.519 GB	small, substantial quality loss
gemma-2-27b-Q4_0.gguf	Q4_0	15.628 GB	legacy; small, very high quality loss - prefer using Q3_K_M
gemma-2-27b-Q4_K_S.gguf	Q4_K_S	15.739 GB	small, greater quality loss
gemma-2-27b-Q4_K_M.gguf	Q4_K_M	16.645 GB	medium, balanced quality - recommended
gemma-2-27b-Q5_0.gguf	Q5_0	18.884 GB	legacy; medium, balanced quality - prefer using Q4_K_M
gemma-2-27b-Q5_K_S.gguf	Q5_K_S	18.884 GB	large, low quality loss - recommended
gemma-2-27b-Q5_K_M.gguf	Q5_K_M	19.408 GB	large, very low quality loss - recommended
gemma-2-27b-Q6_K.gguf	Q6_K	22.344 GB	very large, extremely low quality loss
gemma-2-27b-Q8_0.gguf	Q8_0	28.937 GB	very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/gemma-2-27b-GGUF --include "gemma-2-27b-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/gemma-2-27b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'

Library: transformers
License: gemma
Languages: en
Created: Nov 21, 2024
Updated: Jan 27, 2026