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TuneX

Work In Progress... 🚧

TuneX is a powerful command-line tool designed to provide a comprehensive solution for working with Large Language Models (LLMs). It offers a unified interface for running, fine-tuning, and instruction tuning LLMs, making it an essential utility for researchers and developers in the field of natural language processing and artificial intelligence.

Key features of TuneX include:

  • Support for multiple LLM architectures (GPT2, Llama, Mistral, Gemma)
  • Flexible tokenizer options
  • Chat interface support
  • Various prompt style options
  • Advanced text generation techniques (Top-p, Top-k, Beam Search)
  • Extensive fine-tuning capabilities, including full fine-tuning, adapters, and LoRA
  • Instruction tuning based on human preferences (RLHF, PPO, DPO, RLOO)
  • Comprehensive documentation with examples

TuneX simplifies complex LLM-related tasks through an intuitive command-line interface, allowing users to easily run models, fine-tune on custom datasets, and implement advanced instruction tuning techniques. Whether you're a beginner experimenting with LLMs or an experienced researcher pushing the boundaries of AI, TuneX provides a streamlined, command-line driven approach to support your work.

Installation

pip install tunex

Quick start

# tunex [action] [checkpoit directory / model]
tunex	download  gpt2
tunex	chat      checkpoints/gpt2
tunex	list

Listing Supportive models

tunex list
>>
 _____                __  __
|_   _|   _ _ __   ___\ \/ /
  | || | | | '_ \ / _ \\  / 
  | || |_| | | | |  __//  \ 
  |_| \__,_|_| |_|\___/_/\_\
                            

Supported models: 

1 gpt2
2 gpt2-medium
3 gpt2-large
4 gpt2-xl

Downloading and chatting with models

tunex download "gpt2"
# download the gpt2 model and store it within "checkpoints/gpt2" by default
tunex chat "checkpoints/gpt2"

>>
 _____                __  __
|_   _|   _ _ __   ___\ \/ /
  | || | | | '_ \ / _ \\  / 
  | || |_| | | | |  __//  \ 
  |_| \__,_|_| |_|\___/_/\_\
                            

Initiating chat mode with gpt2

Setting sead to 42

>> Prompt:

General help

# tunex [action] -h
tunex download -h
>>
Download weights or tokenizer data from the Hugging Face Hub.

positional arguments:
  repo_id               The repository ID in the format ``org/name`` or ``user/name`` as shown in Hugging Face. (required, type: str)

optional arguments:
  -h, --help            Show this help message and exit.
  --config CONFIG       Path to a configuration file.
  --print_config[=flags]
                        Print the configuration after applying all other arguments and exit. The optional flags customizes the output and are one or more keywords
                        separated by comma. The supported flags are: comments, skip_default, skip_null.
  --access_token ACCESS_TOKEN
                        Hugging Face API token to access models with restrictions. (type: Union[str, null], default: null)
  --tokenizer_only {true,false}
                        Whether to download only the tokenizer files. (type: bool, default: False)
  --convert_checkpoint {true,false}
                        Whether to convert the checkpoint files from hugging face format after downloading. (type: bool, default: True)
  --checkpoint_dir CHECKPOINT_DIR
                        Where to save the downloaded files. (type: <class 'Path'>, default: checkpoints)
  --model_name MODEL_NAME
                        The existing config name to use for this repo_id. This is useful to download alternative weights of existing architectures. (type:
                        Union[str, null], default: null)

Features Roadmap

  • Multiple LLM support
    • GPT2
    • Llama
    • Mistral
    • Gemma
  • Support for different Tokenizers
  • Chat Interface support
  • Different Prompt Style support
  • Text generation
    • Top-p
    • Top-k
    • Beam Search
  • Finetuning Support with different datasets
    • Full finetuning
    • Adaptars
    • LoRA
  • Instruction Tuning on Human Preferences
    • RLHF
    • PPO
    • DPO
    • RLOO
  • Comprehensive Documentation with example

Acknowledgements