EvolKit is an framework for automatically enhancing the complexity of instructions used in fine-tuning Large Language Models (LLMs). Our project aims to revolutionize the evolution process by leveraging open-source LLMs, moving away from closed-source alternatives.
- Automatic instruction complexity enhancement
- Integration with open-source LLMs
- Streamlined fine-tuning process
- Support for various datasets from Hugging Face
- Flexible configuration options for optimization
To set up EvolKit, follow these steps:
-
Clone the repository:
git clone https://github.com/arcee-ai/EvolKit.git cd EvolKit -
Install the required dependencies:
pip install -r requirements.txt
To run the AutoEvol script, use the following command structure:
python run_evol.py --dataset <dataset_name> [options]
--dataset <dataset_name>: The name of the dataset on Hugging Face to use.--model <model_name>: Model to use for evolving instructions.--generator <generator_type>: Type of generator to use ('openrouter' or 'vllm').--batch_size <int>: Number of instructions to process in each batch.--num_methods <int>: Number of evolution methods to use.--max_concurrent_batches <int>: Maximum number of batches to process concurrently (in our experiment, a cluster of 8xH100 hosting Qwen2-72B-Instruct-GPTQ-Int8 can handle batch size of 50 concurrently).--evolve_epoch <int>: Maximum number of epochs for evolving each instruction.--output_file <filename>: Name of the output file to save results.
--dev_set_size <int>: Number of samples to use in the development set. Use -1 for no devset. Default is -1. (We do not recommend using a dev set since it will take much more time to finish each round)--use_reward_model: Flag to use a reward model for evaluation. No value required.
We found 2 models that work very well with this pipeline:
- Qwen2-72B-Instruct and DeepSeek-V2.5 (GPTQ and AWQ versions are fine too).
- Other models might work but it has to be very good at generating structured content (in order to parse using parsing operations)
To use VLLM as the backend, set the VLLM_BACKEND environment variable:
export VLLM_BACKEND=http://your-vllm-backend-url:port/v1
If not set, it will default to 'http://localhost:8000/v1'.
To run AutoEvol on the 'small_tomb' dataset with custom parameters:
python run_evol.py --dataset qnguyen3/small_tomb --model Qwen/Qwen2-72B-Instruct-GPTQ-Int8 --generator vllm --batch_size 100 --num_methods 3 --max_concurrent_batches 10 --evolve_epoch 3 --output_file the_tomb_evolved-3e-batch100.json --dev_set_size 5 --use_reward_model
This command will:
- Load the 'qnguyen3/small_tomb' dataset from Hugging Face.
- Use the Qwen2-72B-Instruct model with VLLM as the generator.
- Process samples in batches of 100.
- Apply 3 evolution methods for each instruction.
- Process 10 batches concurrently.
- Evolve each instruction for up to 3 epochs.
- Use 5 samples for the development set.
- Use the reward model for evaluation.
- Output the final evolved instructions to the_tomb_evolved-3e-batch100.json.
After evolving the instructions, you can generate answers using:
python gen_answers.py --model Qwen/Qwen2-72B-Instruct-GPTQ-Int8 --generator vllm --data_path the_tomb_evolved-3e-batch100.json --batch_size 50 --output completed_evol_data.json
The final dataset will be saved to completed_evol_data.json in ShareGPT format.
EvolKit consists of several key components:
- Generator: Uses an LLM for generating initial instructions (OpenRouter or VLLM).
- Evolver: Employs a recurrent evolution strategy.
- Analyzer: Utilizes trajectory analysis.
- Evaluator: Offers two options:
- Reward Model Evaluator
- Failure Detector Evaluator (originally from WizardLM's paper)
- Optimizer: Optimizes the evolution method for the next round.
The script saves the results in JSON format to the specified output file. Each entry in the JSON file represents an evolved instruction along with relevant metadata.
Find a 20k subset of a dataset generated using EvolKit here
- Microsoft's WizardLM team for the inspiration from the AutoEvol paper.
