--- base_model: mistralai/Mistral-7B-Instruct-v0.3 license: apache-2.0 tags: - llama-cpp - gguf-my-repo extra_gated_description: If you want to learn more about how we process your personal data, please read our Privacy Policy. --- # Morgen0052/Mistral-7B-Instruct-v0.3-Q8_0-GGUF This model was converted to GGUF format from [`mistralai/Mistral-7B-Instruct-v0.3`](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) 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/mistralai/Mistral-7B-Instruct-v0.3) 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 Morgen0052/Mistral-7B-Instruct-v0.3-Q8_0-GGUF --hf-file mistral-7b-instruct-v0.3-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Morgen0052/Mistral-7B-Instruct-v0.3-Q8_0-GGUF --hf-file mistral-7b-instruct-v0.3-q8_0.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 Morgen0052/Mistral-7B-Instruct-v0.3-Q8_0-GGUF --hf-file mistral-7b-instruct-v0.3-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Morgen0052/Mistral-7B-Instruct-v0.3-Q8_0-GGUF --hf-file mistral-7b-instruct-v0.3-q8_0.gguf -c 2048 ```