Fine-tuning on German-English data with Spectrum Fine-Tuning targeting 15% of the layers.
Utilized unique German-English Sauerkraut Mix v2 dataset for efficient cross-lingual transfer learning
Implemented bespoke, precision-engineered fine-tuning approach to enhance multilingual capabilities
Achieved improved performance in multiple languages (including Arabic, Italian, French, Spanish, Dutch, Portuguese) through cross-lingual knowledge transfer
This model showcases the potential of resource-efficient fine-tuning of large language models using Spectrum Fine-Tuning. Here's a brief on 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
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:
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.
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
Llama-3.1-SauerkrautLM-70b-Instruct-AGIEVAL
GPT4ALL
Llama-3.1-SauerkrautLM-70b-Instruct-GPT4ALL
TRUTHFULQA
Llama-3.1-SauerkrautLM-70b-Instruct-TRUTHFULQA
BBH-HF
Llama-3.1-SauerkrautLM-70b-Instruct-bbh
MMLU-Multilingual
Llama-3.1-SauerkrautLM-70b-Instruct-mmlu
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Contact
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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.
