--- language: - en license: mit tags: - nlp - code - llama-cpp - gguf-my-repo - LMEngine license_link: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/LICENSE pipeline_tag: text-generation inference: parameters: temperature: 0 widget: - messages: - role: user content: Can you provide ways to eat combinations of bananas and dragonfruits? --- # tinybiggames/Phi-3-mini-4k-instruct-Q4_K_M-GGUF 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. Refer to the [original model card](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) for more details on the model. ## Use with tinyBigGAMES's [Inference](https://github.com/tinyBigGAMES) Libraries. How to configure LMEngine: ```Delphi InitConfig( 'C:/LLM/gguf', // path to model files -1 // number of GPU layer, -1 to use all available layers ); ``` How to define model: ```Delphi DefineModel('phi-3-mini-4k-instruct.Q4_K_M.gguf', 'phi-3-mini-4k-instruct.Q4_K_M', 4000, '<|{role}|>{content}<|end|>', '<|assistant|>'); ``` How to add a message: ```Delphi AddMessage( ROLE_USER, // role 'What is AI?' // content ); ``` `{role}` - will be substituted with the message "role" `{content}` - will be substituted with the message "content" How to do inference: ```Delphi var LTokenOutputSpeed: Single; LInputTokens: Int32; LOutputTokens: Int32; LTotalTokens: Int32; if RunInference('phi-3-mini-4k-instruct.Q4_K_M', 1024) then begin GetInferenceStats(nil, @LTokenOutputSpeed, @LInputTokens, @LOutputTokens, @LTotalTokens); PrintLn('', FG_WHITE); PrintLn('Tokens :: Input: %d, Output: %d, Total: %d, Speed: %3.1f t/s', FG_BRIGHTYELLOW, LInputTokens, LOutputTokens, LTotalTokens, LTokenOutputSpeed); end else begin PrintLn('', FG_WHITE); PrintLn('Error: %s', FG_RED, GetError()); end; ```