AutoSynth

Automatically create synthetic data using SOTA techniques (Self Instruct, Magpie, Agent Instruct, Arena Learning, Genstruct, Instruction Synthesizer, Self-Curation) using your LLMs.

## Research log

11-09-2024
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implemented [Instruction Synthesizer](./techniques/instruction_synthesizer/) 
using the instruction-synthesizer pretrained model from huggingface hub. Defined custom pipeline 
steps to generate multiple instruction response pairs from a single text corpus. Fixed the 
multiprocessing conflicts arising when using external models using transformers library in the custom Step sub-classes.

10-09-2024
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implemented [Genstruct](./techniques/genstruct/) technique using the provided distilabel task. 
Used the Genstruct-7B pretrained models to generate instruction-response pairs from raw text corpora.

31-08-2024
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implemented [Magpie](./techniques/magpie/) technique for conversation generation using the 
provided distilabel task. Defined custom distilabel pipeline step to perform list 
splitting and converting conversation to instruction-response pairs.

30-08-2024
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implemented Self-Curation technique for Data Curation as described in this [paper](https://huggingface.co/blog/akjindal53244/llama31-storm8b). 
Used strong models like Meta Llama and Facebook Bert Classifier to separate quality data.

26-08-2024
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implemented [Self Instruct](./techniques/self_instruct/) technique. Coded custom distilabel 
pipeline steps to perform tasks like renaming columns or swapping columns during pipeline processing. 
Used the distilabel Self-Instruct task to generate n instructions from a raw text corpora.

24-08-2024
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implemented [Arena Learning](./techniques/arena_learning/). Used Phi-3 mini, TinyLlama, OpenAI GPT 4o 
mini for testing. It generates multiple answers to a question using different models 
and then ranks them using a stronger model. Used GPT 4o mini for ranking.

22-08-2024
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implemented [Agent Instruct](./techniques/agent_instruct/). Used Phi-3 mini for testing. It generates instructions using multiple agents.

✅ Techniques

1. Self Instruct
2. Magpie
3. Agent Instruct
4. Self-Curation
5. Genstruct
6. Instruction Synthesizer
7. Arena Learning

Setup

1. Install Dependencies:

pip install -r requirements.txt

2. Setup configs:

The directory of each technique in techniques contains an example config.yaml file. Adjust the values of datasets, models, and other parameters in that file.

3. Run the main.py script: Specify the config.yaml file path and the technique name while runnning this command.

python main.py --config=./techniques/genstruct/config.yaml --technique=genstruct

For help run,

python main.py --help

🦋 Citation

If you find this code useful, please cite the following:

@misc{Zain2024AutoSynth,
  author = {Zain, Abideen},
  title = {AutoSynth},
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/abideenml/AutoSynth}},
}

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