fblgit/LUNA-SOLARkrautLM-Instruct
LUNA-SOLARkrautLM-Instruct is a 10.7 billion parameter instruction-tuned causal language model developed by fblgit, based on the SOLAR-10.7B-Instruct-v1.0 architecture. It is aligned with DPO and enhanced with UNA, specifically optimized for improved German language capabilities through data augmentation and a German DPO dataset. This model excels in both English and German, making it suitable for multilingual applications requiring polite and competent responses.
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LUNA-SOLARkrautLM-Instruct Overview
LUNA-SOLARkrautLM-Instruct is a 10.7 billion parameter model developed by fblgit, building upon the upstage/SOLAR-10.7B-Instruct-v1.0 base model. It has been aligned using DPO (Direct Preference Optimization) and integrated with UNA technology. A key focus during its development was enhancing its German language proficiency through a unique training approach.
Key Capabilities
- Multilingual Proficiency: Supports both English and German, with a particular emphasis on improved German language skills.
- DPO Alignment: Trained with a custom German SauerkrautLM-DPO dataset, augmented with translated portions of
HuggingFaceH4/ultrafeedback_binarizedandargilla/distilabel-math-preference-dpo. - Data Augmentation: Utilizes data augmentation techniques to ensure grammatical and syntactical correctness, and a more natural German wording in its responses.
- Contamination-Free Dataset: Rigorous data contamination tests confirm the SauerkrautLM-DPO dataset is free from unwanted data, with results well below 0.9% across ARC, MMLU, TruthfulQA, and GSM8K.
Good For
- Applications requiring a capable instruction-following model in both English and German.
- Use cases where natural and grammatically correct German phrasing is crucial.
- Developers seeking a DPO-aligned model with a strong focus on German language quality.
Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.