--- language: - en - zh - id - th - vi - ms - lo datasets: - cerebras/SlimPajama-627B - Skywork/SkyPile-150B - allenai/MADLAD-400 - cc100 tags: - multilingual - sea - sailor - llama-cpp - gguf-my-repo license: apache-2.0 base_model: sail/Sailor-1.8B inference: false model-index: - name: Sailor-1.8B results: - task: type: text-generation dataset: name: XQuAD-Thai type: XQuAD-Thai metrics: - type: EM (3-Shot) value: 32.72 name: EM (3-Shot) - type: F1 (3-Shot) value: 48.66 name: F1 (3-Shot) - task: type: text-generation dataset: name: TyDiQA-Indonesian type: TyDiQA-Indonesian metrics: - type: EM (3-Shot) value: 40.88 name: EM (3-Shot) - type: F1 (3-Shot) value: 65.37 name: F1 (3-Shot) - task: type: text-generation dataset: name: XQuAD-Vietnamese type: XQuAD-Vietnamese metrics: - type: EM (3-Shot) value: 34.22 name: EM (3-Shot) - type: F1 (3-Shot) value: 53.35 name: F1 (3-Shot) - task: type: text-generation dataset: name: XCOPA-Thai type: XCOPA-Thai metrics: - type: EM (3-Shot) value: 53.8 name: EM (3-Shot) - task: type: text-generation dataset: name: XCOPA-Indonesian type: XCOPA-Indonesian metrics: - type: EM (3-Shot) value: 64.2 name: EM (3-Shot) - task: type: text-generation dataset: name: XCOPA-Vietnamese type: XCOPA-Vietnamese metrics: - type: EM (3-Shot) value: 63.2 name: EM (3-Shot) - task: type: text-generation dataset: name: M3Exam-Thai type: M3Exam-Thai metrics: - type: EM (3-Shot) value: 25.38 name: EM (3-Shot) - task: type: text-generation dataset: name: M3Exam-Indonesian type: M3Exam-Indonesian metrics: - type: EM (3-Shot) value: 28.3 name: EM (3-Shot) - task: type: text-generation dataset: name: M3Exam-Vietnamese type: M3Exam-Vietnamese metrics: - type: EM (3-Shot) value: 34.71 name: EM (3-Shot) - task: type: text-generation dataset: name: BELEBELE-Thai type: BELEBELE-Thai metrics: - type: EM (3-Shot) value: 34.22 name: EM (3-Shot) - task: type: text-generation dataset: name: BELEBELE-Indonesian type: BELEBELE-Indonesian metrics: - type: EM (3-Shot) value: 34.89 name: EM (3-Shot) - task: type: text-generation dataset: name: BELEBELE-Vietnamese type: BELEBELE-Vietnamese metrics: - type: EM (3-Shot) value: 35.33 name: EM (3-Shot) --- # AIronMind/Sailor-1.8B-Q4_K_M-GGUF This model was converted to GGUF format from [`sail/Sailor-1.8B`](https://huggingface.co/sail/Sailor-1.8B) 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/sail/Sailor-1.8B) 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 AIronMind/Sailor-1.8B-Q4_K_M-GGUF --hf-file sailor-1.8b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo AIronMind/Sailor-1.8B-Q4_K_M-GGUF --hf-file sailor-1.8b-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 AIronMind/Sailor-1.8B-Q4_K_M-GGUF --hf-file sailor-1.8b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo AIronMind/Sailor-1.8B-Q4_K_M-GGUF --hf-file sailor-1.8b-q4_k_m.gguf -c 2048 ```