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README.md
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---
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library_name: peft
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tags:
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- llama-cpp
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- gguf-my-repo
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base_model: IlyaGusev/saiga_phi3_medium_sft_m1_d2
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---
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# VlSav/saiga_phi3_medium_sft_m1_d2-Q8_0-GGUF
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This model was converted to GGUF format from [`IlyaGusev/saiga_phi3_medium_sft_m1_d2`](https://huggingface.co/IlyaGusev/saiga_phi3_medium_sft_m1_d2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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Refer to the [original model card](https://huggingface.co/IlyaGusev/saiga_phi3_medium_sft_m1_d2) for more details on the model.
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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 --hf-repo VlSav/saiga_phi3_medium_sft_m1_d2-Q8_0-GGUF --hf-file saiga_phi3_medium_sft_m1_d2-q8_0.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 VlSav/saiga_phi3_medium_sft_m1_d2-Q8_0-GGUF --hf-file saiga_phi3_medium_sft_m1_d2-q8_0.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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./main --hf-repo VlSav/saiga_phi3_medium_sft_m1_d2-Q8_0-GGUF --hf-file saiga_phi3_medium_sft_m1_d2-q8_0.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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./server --hf-repo VlSav/saiga_phi3_medium_sft_m1_d2-Q8_0-GGUF --hf-file saiga_phi3_medium_sft_m1_d2-q8_0.gguf -c 2048
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```
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