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zw89/gemma-2-2b-it-Q4_K_M-GGUF overview

BafS/gemma 2 2b it Q4 K M GGUF This model was converted to GGUF format from google/gemma 2 2b it https://huggingface.co/google/gemma 2 2b it using llama.cpp vi…

transformersggufconversationalllama-cppgguf-my-repotext-generationbase_model:google/gemma-2-2b-itbase_model:quantized:google/gemma-2-2b-itlicense:gemmaendpoints_compatibleregion:us

Runs locally from ~1.59 GB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

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text-generation
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Repository Files & Downloads

1 GGUF files detected
Direct downloads for local inference
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gemma-2-2b-it-q4_k_m.ggufGGUFQ4_K_M1.59 GBDownload

Model Details

Model IDzw89/gemma-2-2b-it-Q4_K_M-GGUF
Authorzw89
Pipelinetext-generation
Licensegemma
Base modelgoogle/gemma-2-2b-it
Last modified2026-08-28T08:16:58.000Z

Model README

---

base_model: google/gemma-2-2b-it

library_name: transformers

license: gemma

pipeline_tag: text-generation

tags:

  • conversational
  • llama-cpp
  • gguf-my-repo

extra_gated_heading: Access Gemma on Hugging Face

extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and

agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging

Face and click below. Requests are processed immediately.

extra_gated_button_content: Acknowledge license

---

BafS/gemma-2-2b-it-Q4_K_M-GGUF

This model was converted to GGUF format from google/gemma-2-2b-it using llama.cpp via the ggml.ai's GGUF-my-repo space.

Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo BafS/gemma-2-2b-it-Q4_K_M-GGUF --hf-file gemma-2-2b-it-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo BafS/gemma-2-2b-it-Q4_K_M-GGUF --hf-file gemma-2-2b-it-q4_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo BafS/gemma-2-2b-it-Q4_K_M-GGUF --hf-file gemma-2-2b-it-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo BafS/gemma-2-2b-it-Q4_K_M-GGUF --hf-file gemma-2-2b-it-q4_k_m.gguf -c 2048

Run zw89/gemma-2-2b-it-Q4_K_M-GGUF with guIDE

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