-- https://colab.research.google.com/drive/1c7oHSEemh8Ih4ExRd4bQ6WHADVY-Mfj1#scrollTo=ADmdVbV93wpy - base_model: silma-ai/SILMA-9B-Instruct-v1.0 language: - ar - en library_name: transformers license: gemma pipeline_tag: text-generation tags: - conversational - llama-cpp - gguf-my-repo extra_gated_button_content: Acknowledge license model-index: - name: SILMA-9B-Instruct-v1.0 results: - task: type: text-generation dataset: name: MMLU (Arabic) type: OALL/Arabic_MMLU metrics: - type: loglikelihood_acc_norm value: 52.55 name: acc_norm source: url: https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard name: Open Arabic LLM Leaderboard - task: type: text-generation dataset: name: AlGhafa type: OALL/AlGhafa-Arabic-LLM-Benchmark-Native metrics: - type: loglikelihood_acc_norm value: 71.85 name: acc_norm source: url: https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard name: Open Arabic LLM Leaderboard - task: type: text-generation dataset: name: ARC Challenge (Arabic) type: OALL/AlGhafa-Arabic-LLM-Benchmark-Translated metrics: - type: loglikelihood_acc_norm value: 78.19 name: acc_norm - type: loglikelihood_acc_norm value: 86 name: acc_norm - type: loglikelihood_acc_norm value: 64.05 name: acc_norm - type: loglikelihood_acc_norm value: 78.89 name: acc_norm - type: loglikelihood_acc_norm value: 47.64 name: acc_norm - type: loglikelihood_acc_norm value: 72.93 name: acc_norm - type: loglikelihood_acc_norm value: 71.96 name: acc_norm - type: loglikelihood_acc_norm value: 75.55 name: acc_norm - type: loglikelihood_acc_norm value: 91.26 name: acc_norm - type: loglikelihood_acc_norm value: 67.59 name: acc_norm source: url: https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard name: Open Arabic LLM Leaderboard - task: type: text-generation dataset: name: ACVA type: OALL/ACVA metrics: - type: loglikelihood_acc_norm value: 78.89 name: acc_norm source: url: https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard name: Open Arabic LLM Leaderboard - task: type: text-generation dataset: name: Arabic_EXAMS type: OALL/Arabic_EXAMS metrics: - type: loglikelihood_acc_norm value: 51.4 name: acc_norm source: url: https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard name: Open Arabic LLM Leaderboard --- # goodasdgood/SILMA-9B-Instruct-v1.0-IQ4_NL-GGUF This model was converted to GGUF format from [`silma-ai/SILMA-9B-Instruct-v1.0`](https://huggingface.co/silma-ai/SILMA-9B-Instruct-v1.0) 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/silma-ai/SILMA-9B-Instruct-v1.0) 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 goodasdgood/SILMA-9B-Instruct-v1.0-IQ4_NL-GGUF --hf-file silma-9b-instruct-v1.0-iq4_nl-imat.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo goodasdgood/SILMA-9B-Instruct-v1.0-IQ4_NL-GGUF --hf-file silma-9b-instruct-v1.0-iq4_nl-imat.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 goodasdgood/SILMA-9B-Instruct-v1.0-IQ4_NL-GGUF --hf-file silma-9b-instruct-v1.0-iq4_nl-imat.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo goodasdgood/SILMA-9B-Instruct-v1.0-IQ4_NL-GGUF --hf-file silma-9b-instruct-v1.0-iq4_nl-imat.gguf -c 2048 ```