Self-instruct tool for LLM finetuning
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Airoboros is a tool for automating the creation of high-quality datasets for fine-tuning large language models (LLMs). It addresses the challenge of data scarcity by enabling users to generate diverse and task-specific instruction datasets using LLMs, aiming to empower individuals and smaller organizations to build specialized AI models without the prohibitive costs of large-scale data curation or reliance on proprietary models.
How It Works
Airoboros implements a modified "self-instruct" approach, leveraging LLMs to generate synthetic instruction-following data. Key differentiators include support for OpenAI's chat completion API (enabling cost-effective use of GPT-3.5-turbo and GPT-4), customizable topic generation, and an in-memory vector database (Chroma) for efficient similarity comparisons. The system utilizes asyncio
for concurrent data generation with configurable batch sizes and employs specialized "instructors" to create data for various use cases like reasoning, role-playing, and function calling, ensuring context relevance.
Quick Start & Requirements
pip install --no-build-isolation airoboros
or pip install -e --no-build-isolation ./airoboros
from a cloned repository.pip install -U --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu118
).Highlighted Details
/v1/completions
and /v1/chat/completions
.Maintenance & Community
Licensing & Compatibility
Limitations & Caveats
The project relies heavily on OpenAI's API for its core data generation functionality, which may not be suitable for fully open-source or air-gapped environments. Some components, like the vllm server, are noted as having "not quite as good" results. The "research use only" designation for certain model datasets requires careful consideration for commercial applications.
1 year ago
1 day