quangvd8x/gemma-3-1b-it-Q4_K_M-GGUF overview
quangvd8x/gemma 3 1b it Q4 K M GGUF This model was converted to GGUF format from google/gemma 3 1b it https://huggingface.co/google/gemma 3 1b it using llama.c…
Runs locally from ~768.7 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| gemma-3-1b-it-q4_k_m.gguf | GGUF | Q4_K_M | 768.7 MB | Download |
Model Details
| Model ID | quangvd8x/gemma-3-1b-it-Q4_K_M-GGUF |
|---|---|
| Author | quangvd8x |
| Pipeline | text-generation |
| License | gemma |
| Base model | google/gemma-3-1b-it |
| Last modified | 2026-06-12T08:45:08.000Z |
Model README
---
license: gemma
library_name: transformers
pipeline_tag: text-generation
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
base_model: google/gemma-3-1b-it
tags:
- llama-cpp
- gguf-my-repo
---
quangvd8x/gemma-3-1b-it-Q4_K_M-GGUF
This model was converted to GGUF format from google/gemma-3-1b-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 quangvd8x/gemma-3-1b-it-Q4_K_M-GGUF --hf-file gemma-3-1b-it-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo quangvd8x/gemma-3-1b-it-Q4_K_M-GGUF --hf-file gemma-3-1b-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 quangvd8x/gemma-3-1b-it-Q4_K_M-GGUF --hf-file gemma-3-1b-it-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo quangvd8x/gemma-3-1b-it-Q4_K_M-GGUF --hf-file gemma-3-1b-it-q4_k_m.gguf -c 2048Run quangvd8x/gemma-3-1b-it-Q4_K_M-GGUF with guIDE
Download guIDE — the AI-native code editor with local LLM inference and 69 built-in tools.
Source: Hugging Face · Compare models