README.md

metadata

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 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with tinyBigGAMES's Inference Libraries.

How to configure LMEngine:

InitConfig(
 'C:/LLM/gguf', // path to model files
 -1             // number of GPU layer, -1 to use all available layers
);

How to define model:

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:

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:

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;