Gemma 4 E4B It Q4 K M GGUF

--- library_name: transformers license: apache-2.0 license_link: https://ai.google.dev/gemma/docs/gemma_4_license pipeline_tag: any-to-any base_model: google/gemma-4-E4B-it tags: - llama-cpp - gguf-my-repo --- # MHAIDataArchitect/gemma-4-E4B-it-Q4_K_M-GGUF This model was converted to GGUF format from [`google/gemma-4-E4B-it`](https://huggingface.co/google/gemma-4-E4B-it) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/google/gemma-4-E4B-it) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo MHAIDataArchitect/gemma-4-E4B-it-Q4_K_M-GGUF --hf-file gemma-4-e4b-it-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo MHAIDataArchitect/gemma-4-E4B-it-Q4_K_M-GGUF --hf-file gemma-4-e4b-it-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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 MHAIDataArchitect/gemma-4-E4B-it-Q4_K_M-GGUF --hf-file gemma-4-e4b-it-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo MHAIDataArchitect/gemma-4-E4B-it-Q4_K_M-GGUF --hf-file gemma-4-e4b-it-q4_k_m.gguf -c 2048 ```

Source context: 6 downloads · 0 likes · Pipeline any-to-any · Library transformers · Repo MHAIDataArchitect/gemma-4-E4B-it-Q4_K_M-GGUF