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+ ---
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+ base_model: AdnanRiaz107/SFTCodePhi1
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+ datasets:
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+ - apps
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+ license: mit
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+ tags:
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+ - trl
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+ - sft
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+ - generated_from_trainer
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+ - llama-cpp
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+ - gguf-my-repo
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+ model-index:
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+ - name: SFTCodePhi1
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+ results: []
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+ ---
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+
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+ # AdnanRiaz107/SFTCodePhi1-Q4_K_M-GGUF
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+ This model was converted to GGUF format from [`AdnanRiaz107/SFTCodePhi1`](https://huggingface.co/AdnanRiaz107/SFTCodePhi1) 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/AdnanRiaz107/SFTCodePhi1) 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)
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+
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+ ```bash
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+ brew install llama.cpp
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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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+ llama-cli --hf-repo AdnanRiaz107/SFTCodePhi1-Q4_K_M-GGUF --hf-file sftcodephi1-q4_k_m.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 AdnanRiaz107/SFTCodePhi1-Q4_K_M-GGUF --hf-file sftcodephi1-q4_k_m.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 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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+
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+ Step 3: Run inference through the main binary.
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+ ```
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+ ./llama-cli --hf-repo AdnanRiaz107/SFTCodePhi1-Q4_K_M-GGUF --hf-file sftcodephi1-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 AdnanRiaz107/SFTCodePhi1-Q4_K_M-GGUF --hf-file sftcodephi1-q4_k_m.gguf -c 2048
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+ ```