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+ ---
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+ base_model: google/gemma-2-9b
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+ library_name: transformers
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+ license: other
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+ tags:
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+ - llama-cpp
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+ - gemma
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+ - gemma-2-9b
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+ - gemma-9b
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+ - GGUF
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+ ---
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+
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+ # Sri-Vigneshwar-DJ/gemma-2-9b-marketing-content-GGUF
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+ This model was converted to GGUF format from [`google/gemma-2-9b`](https://huggingface.co/google/gemma-2-9b) using llama.cpp
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+ Refer to the [original model card](https://huggingface.co/google/gemma-2-9b) for more details on the model.
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+
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+ ## Use with llama.cpp
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+ Install llama.cpp through brew (works on Mac and Linux) from []
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+
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+ ```bash
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+ brew install llama.cpp or !git clone https://github.com/ggerganov/llama.cpp.git
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+
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+ ```
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+ Invoke the llama.cpp server or the CLI.
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+
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+ ### CLI:
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+ ```bash
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+ ! /content/llama.cpp/llama-cli -m ./sarvam-2b-v0.5-GGUF -n 90 --repeat_penalty 1.0 --color -i -r "User:" -f /content/llama.cpp/prompts/chat-with-bob.txt
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+
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+ or
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+
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+ llama-cli --hf-repo Sri-Vigneshwar-DJ/sarvam-2b-v0.5-GGUF --hf-file FP8.gguf -p "The meaning to life and the universe is"
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+ ```
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+
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+ ### Server:
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+ ```bash
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+ llama-server --hf-repo Sri-Vigneshwar-DJ/gemma-2-9b-marketing-content-GGUF --hf-file FP8.gguf -c 2048
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+ ```
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+
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+ 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.
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+
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+ Step 1: Clone llama.cpp from GitHub.
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+ ```
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+ git clone https://github.com/ggerganov/llama.cpp
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+ ```
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+
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+ Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag or ''!make GGML_OPENBLAS=1' along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
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+ ```
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+ cd llama.cpp && LLAMA_CURL=1 make
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+
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+ or
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+
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+ !make GGML_OPENBLAS=1
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+ ```
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+
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+ Step 3: Run inference through the main binary.
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+ ```
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+ ./llama-cli --hf-repo Sri-Vigneshwar-DJ/gemma-2-9b-marketing-content-GGUF --hf-file FP8.gguf -p "The meaning to life and the universe is"
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+ ```
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+ or
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+ ```
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+ ./llama-server --hf-repo Sri-Vigneshwar-DJ/gemma-2-9b-marketing-content-GGUF --hf-file sFP8.gguf -c 2048
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+ ```