tinybiggames
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README.md
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---
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language:
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- en
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license: mit
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tags:
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- nlp
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- code
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- llama-cpp
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- gguf-my-repo
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- LMEngine
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license_link: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/LICENSE
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pipeline_tag: text-generation
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inference:
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parameters:
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temperature: 0
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widget:
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- messages:
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- role: user
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# tinybiggames/Phi-3-mini-4k-instruct-Q4_K_M-GGUF
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This model was converted to GGUF format from [`microsoft/Phi-3-mini-4k-instruct`](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) 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/microsoft/Phi-3-mini-4k-instruct) for more details on the model.
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## Use with tinyBigGAMES's [Inference](https://github.com/tinyBigGAMES) Libraries.
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```Delphi
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InitConfig(
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'C:/LLM/gguf', // path to model files
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-1 // number of GPU layer, -1 to use all available layers
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);
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```
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'<|{role}|>{content}<|end|>',
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'<|assistant|>');
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```
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'What is AI?' // content
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);
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```
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`
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begin
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GetInferenceStats(nil, @LTokenOutputSpeed, @LInputTokens, @LOutputTokens,
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@LTotalTokens);
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PrintLn('', FG_WHITE);
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PrintLn('Tokens :: Input: %d, Output: %d, Total: %d, Speed: %3.1f t/s',
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FG_BRIGHTYELLOW, LInputTokens, LOutputTokens, LTotalTokens, LTokenOutputSpeed);
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end
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else
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begin
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PrintLn('', FG_WHITE);
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PrintLn('Error: %s', FG_RED, GetError());
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end;
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```
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---
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base_model: microsoft/Phi-3-mini-4k-instruct
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language:
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- en
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license: mit
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license_link: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/LICENSE
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pipeline_tag: text-generation
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tags:
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- nlp
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- code
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- llama-cpp
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- gguf-my-repo
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inference:
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parameters:
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temperature: 0.0
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widget:
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- messages:
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- role: user
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# tinybiggames/Phi-3-mini-4k-instruct-Q4_K_M-GGUF
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This model was converted to GGUF format from [`microsoft/Phi-3-mini-4k-instruct`](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) 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/microsoft/Phi-3-mini-4k-instruct) 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-cli --hf-repo tinybiggames/Phi-3-mini-4k-instruct-Q4_K_M-GGUF --hf-file phi-3-mini-4k-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 tinybiggames/Phi-3-mini-4k-instruct-Q4_K_M-GGUF --hf-file phi-3-mini-4k-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 tinybiggames/Phi-3-mini-4k-instruct-Q4_K_M-GGUF --hf-file phi-3-mini-4k-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 tinybiggames/Phi-3-mini-4k-instruct-Q4_K_M-GGUF --hf-file phi-3-mini-4k-instruct-q4_k_m.gguf -c 2048
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```
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