Introduction to the Tiger Project: An Open Source LLM Toolkit
Overview of TigerLab
TigerLab presents an innovative and open-source toolkit designed to support the development of trustworthy Large Language Models (LLMs). With a focus on creating reliable artificial intelligence, TigerLab embraces concepts such as Retrieval-Augmented Generation (RAG), refinement through fine-tuning, and metrics for AI safety.
Key Features
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TigerRAG: This component employs embeddings-based retrieval, RAG, and generation-augmented retrieval (GAR) to enhance information recall and generation. It utilizes BERT for embedding, FAISS for indexing, and text-generative models to deliver precise and contextual answers.
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TigerTune: An intuitive Python SDK, TigerTune facilitates the fine-tuning of text generation and classification models. It demonstrates effective model adaptation, as shown in demos featuring Llama2 and DistilBERT.
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TigerDA: A comprehensive Data Augmentation Toolkit, TigerDA leverages fine-tuned models like GPT2 for augmenting datasets, enhancing model training through varied data inputs. It soon plans to include perturbation-based augmenters for broader utility.
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TigerArmor: Prioritizing AI safety, this toolkit offers metrics, datasets, and evaluation tools essential for assessing the security of LLMs, including prominent models like Llama 2 and GPT-4.
User Engagement and Community
TigerLab actively engages with its community through several platforms:
- Discord: Users can join the conversation for real-time support and collaboration.
- GitHub: Enthusiasts are invited to contribute code, resolve issues, and partake in feature development.
- Twitter: Follow updates and interact with the development team.
Demonstrations and Tutorials
TigerLab offers practical demonstrations to showcase its capabilities:
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Enhanced Retrieval: Using EBR, RAG, and GAR, this demo reveals how TigerLab can improve data retrieval processes, ensuring contextually relevant results.
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Fine-tuning Models: Explores the adaptation of models like Llama2 and DistilBERT, illustrating TigerLab’s approach to personalized AI solutions.
For users looking to get started, a detailed setup tutorial is available on YouTube.
Technical Requirements
To operate TigerLab efficiently, users must obtain an OpenAI API token, which enables access to the language model’s capabilities. The installation involves cloning the repository and setting up individual components like TigerRAG, TigerTune, and TigerDA, each with specific dependencies.
Future Roadmap
TigerLab's roadmap includes several upcoming enhancements:
- Introduction of new model support in TigerTune
- Expansion of TigerDA with perturbation-based augmenters
- Comparisons of GPT Text Completion Models
- Initiatives like an AI Safety Leaderboard for LLMs
Conclusion
TigerLab stands as a pioneering framework in the realm of Large Language Models, aimed at bridging the gap between general AI models and specific contextual data. Its development tools empower users to craft AI systems that align with their distinct operational and safety requirements. As TigerLab continues to evolve, it remains committed to advancing the precision and security of AI technologies.