Llama 3.1 SauerkrautLM 70b Instruct

🚀 VAGO solutions Llama-3.1-SauerkrautLM-70b-Instruct

Fine-tuned Model - Showcasing the potential of resource-efficient fine-tuning of large language models using Spectrum Fine-Tuning

Introducing Llama-3.1-SauerkrautLM-70b-Instruct – our Sauerkraut version of the powerful meta-llama/Meta-Llama-3.1-70B-Instruct! This model is fine-tuned on German-English data, leveraging unique datasets and a precision-engineered approach to enhance multilingual capabilities and achieve better performance across multiple languages.

🚀 Quick Start

This section provides a high - level overview of the Llama-3.1-SauerkrautLM-70b-Instruct model. For detailed information, please refer to the subsequent sections.

✨ Features

• Efficient Fine - Tuning: Fine-tuning on German-English data with Spectrum Fine-Tuning, targeting 15% of the layers.
• Unique Dataset: Utilized the unique German-English Sauerkraut Mix v2 dataset for efficient cross-lingual transfer learning.
• Precision Approach: Implemented a bespoke, precision-engineered fine-tuning approach to enhance multilingual capabilities.
• Cross - Lingual Transfer: Achieved improved performance in multiple languages (including Arabic, Italian, French, Spanish, Dutch, Portuguese) through cross-lingual knowledge transfer.

📚 Documentation

🔍 Overview of all Llama-3.1-SauerkrautLM-70b-Instruct

📋 Model Details

🔧 Training Procedure

This model showcases the potential of resource-efficient fine-tuning of large language models using Spectrum Fine-Tuning. Here's a detailed look at the procedure:

🎯 Fine-tuning on German-English Data

• Utilized Spectrum Fine-Tuning, targeting 15% of the model's layers.
• Introduced the model to a unique German-English Sauerkraut Mix v2.
• Implemented a bespoke, precision-engineered fine-tuning approach.

🌐 Cross-lingual Transfer Learning using Sauerkraut Mix v2

• Leveraged the Sauerkraut Mix v2 dataset as the foundation for cross-lingual transfer.
• This unique dataset, primarily focused on German and English, enabled the model to transfer knowledge to other languages.
• Improved capabilities in Arabic, Italian, French, Spanish, Dutch, and Portuguese without extensive training data in each language.
• Demonstrated the effectiveness of using a bilingual dataset for multilingual improvement.

📦 Sauerkraut Mix v2

• Premium Dataset for Language Models, focusing on German and English.
• Meticulously selected, high-quality dataset combinations.
• Cutting-edge synthetic datasets created using proprietary, high-precision generation techniques.
• Serves as the core resource for both fine-tuning and cross-lingual transfer.

🎯 Objective and Results

The primary goal of this training was twofold:

1. To demonstrate that Spectrum Fine-Tuning, targeting just 15% of the layers, can significantly enhance a 70 - billion parameter model's capabilities while using only a fraction of the resources required by classic fine-tuning approaches.
2. To showcase the effectiveness of cross-lingual transfer learning using the Sauerkraut Mix v2 dataset, enabling multilingual improvement without extensive language-specific training data.

The results have been remarkable:

• The model has substantially improved its multilingual skills, as demonstrated by impressive benchmarks on MMLU Multilingual.

Key Findings:

• Spectrum Fine-Tuning can efficiently enhance a large language model's capabilities in multiple languages while preserving the majority of its previously acquired knowledge.
• The Sauerkraut Mix v2 dataset proves to be an effective foundation for cross-lingual transfer, allowing for multilingual improvements from a bilingual base.
• This approach demonstrates a resource-efficient method for creating powerful multilingual models without the need for extensive training data in each target language.

📊 Evaluation

• AGIEVAL
• GPT4ALL
• TRUTHFULQA
• BBH-HF
• MMLU-Multilingual

⚠️ Disclaimer

⚠️ Important Note

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📞 Contact

💡 Usage Tip

If you are interested in customized LLMs for business applications, please get in contact with us via our website. We are also grateful for your feedback and suggestions.

🤝 Collaborations

We are also keenly seeking support and investment for our startup, VAGO solutions where we continuously advance the development of robust language models designed to address a diverse range of purposes and requirements. If the prospect of collaboratively navigating future challenges excites you, we warmly invite you to reach out to us at VAGO solutions

🙏 Acknowledgement

Many thanks to meta-llama for providing such a valuable model to the Open-Source community.