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--- |
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library_name: transformers |
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license: apache-2.0 |
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language: |
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- en |
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tags: |
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- llama-cpp |
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- gguf |
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- quantized |
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- smol |
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- tulu |
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base_model: |
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- SultanR/SmolTulu-1.7b-Instruct |
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pipeline_tag: text-generation |
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--- |
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# SmolTulu.GGUF |
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This is the GGUF version of [SmolTulu-1.7b-Instruct](https://huggingface.co/SultanR/SmolTulu-1.7b-Instruct), quantized to Q4_K_M! |
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## Use with llama.cpp |
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Install llama.cpp through brew (works on Mac and Linux) |
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```bash |
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brew install llama.cpp |
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``` |
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Invoke the llama.cpp server or the CLI. |
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### CLI: |
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```bash |
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llama-cli --hf-repo SultanR/SmolTulu-1.7b-Instruct-GGUF --hf-file smoltulu-1.7b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" |
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``` |
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### Server: |
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```bash |
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llama-server --hf-repo SultanR/SmolTulu-1.7b-Instruct-GGUF --hf-file smoltulu-1.7b-instruct-q4_k_m.gguf -c 2048 |
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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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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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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). |
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``` |
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cd llama.cpp && LLAMA_CURL=1 make |
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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 SultanR/SmolTulu-1.7b-Instruct-GGUF --hf-file smoltulu-1.7b-instruct-q4_k_m.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 SultanR/SmolTulu-1.7b-Instruct-GGUF --hf-file smoltulu-1.7b-instruct-q4_k_m.gguf -c 2048 |
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``` |