--- library_name: transformers datasets: - hardikg2907/cleaned-dataset-1-500k language: - en base_model: hardikg2907/code-llama-html-completion-1 tags: - llama-cpp - gguf-my-repo --- # hardikg2907/code-llama-html-completion-1-Q4_K_S-GGUF This model was converted to GGUF format from [`hardikg2907/code-llama-html-completion-1`](https://huggingface.co/hardikg2907/code-llama-html-completion-1) 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/hardikg2907/code-llama-html-completion-1) 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 hardikg2907/code-llama-html-completion-1-Q4_K_S-GGUF --hf-file code-llama-html-completion-1-q4_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo hardikg2907/code-llama-html-completion-1-Q4_K_S-GGUF --hf-file code-llama-html-completion-1-q4_k_s.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 hardikg2907/code-llama-html-completion-1-Q4_K_S-GGUF --hf-file code-llama-html-completion-1-q4_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo hardikg2907/code-llama-html-completion-1-Q4_K_S-GGUF --hf-file code-llama-html-completion-1-q4_k_s.gguf -c 2048 ```