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Tülu 3
Overview :
Tülu 3 is a series of open-source advanced language models that have been fine-tuned to adapt to various tasks and user needs. These models achieve complex training processes by combining elements of proprietary methods, innovative technology, and established academic research. The success of Tülu 3 is rooted in meticulous data management, rigorous experimentation, innovative methodologies, and enhanced training infrastructure. By openly sharing data, recipes, and findings, Tülu 3 aims to empower the community to explore new and innovative fine-tuning techniques.
Target Users :
The target audience includes researchers, developers, AI practitioners, and entrepreneurs. Tülu 3 is suitable for them as it allows fine-tuning open-source models for their specific use cases, achieving quality that rivals proprietary models. Developers and AI builders can now leverage Tülu 3's data and recipes to adapt to their datasets without losing the core skills necessary for utilizing Tülu 3's resources effectively.
Use Cases
Researchers can use Tülu 3 to train a model capable of understanding and generating code.
Developers can leverage Tülu 3's datasets and recipes to create a multilingual interactive chatbot.
Entrepreneurs can fine-tune a model to perform domain-specific reasoning tailored to their business needs.
Features
Offers extensive fine-tuning data and tools to advance the frontier of open fine-tuning.
Includes expanded guidance covering evaluation, decontamination, and recipe design.
Introduces new synthetic instruction datasets and expands preference data through strategic generation.
Utilizes verifiable reward-based reinforcement learning, a novel approach to enhance specific skills without the need for a reward model.
Releases models of various sizes and all checkpoints for users to apply directly or customize their fine-tuning.
Provides an evaluation framework that allows developers to specify all settings and easily replicate all evaluations conducted by Tülu 3.
Makes all infrastructure code publicly available, aiding users in setting up the entire process from data selection to evaluation.
How to Use
1. Visit the Tülu 3 GitHub page to download the required models and datasets.
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2. Select the appropriate dataset for fine-tuning based on the provided recipes.
3. Set up the entire fine-tuning process using the infrastructure code provided by Tülu 3.
4. Evaluate the fine-tuned model using the supplied assessment framework.
5. Adjust model parameters as necessary to achieve optimal performance and results.
6. Deploy the trained model into real-world applications, such as chatbots or code generators.
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