--- base_model: cosimoiaia/Loquace-7B datasets: - cosimoiaia/Loquace-102k language: - it license: cc-by-nc-2.0 pipeline_tag: conversational tags: - alpaca - llama - llm - finetune - Italian - qlora - llama-cpp - gguf-my-repo --- # antoste/Loquace-7B-Q3_K_L-GGUF This model was converted to GGUF format from [`cosimoiaia/Loquace-7B`](https://huggingface.co/cosimoiaia/Loquace-7B) 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/cosimoiaia/Loquace-7B) 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 antoste/Loquace-7B-Q3_K_L-GGUF --hf-file loquace-7b-q3_k_l.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo antoste/Loquace-7B-Q3_K_L-GGUF --hf-file loquace-7b-q3_k_l.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 antoste/Loquace-7B-Q3_K_L-GGUF --hf-file loquace-7b-q3_k_l.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo antoste/Loquace-7B-Q3_K_L-GGUF --hf-file loquace-7b-q3_k_l.gguf -c 2048 ```