Empowering Multilingual AI for Europe
MiniLingua: A Small Open-Source LLM for European Languages
This paper introduces MiniLingua, a multilingual open-source LLM designed for 13 European languages, balancing coverage and instruction-following capabilities with a compact one-billion-parameter architecture.
Executive Impact Summary
MiniLingua outperforms EuroLLM on key NLP tasks and remains competitive with larger state-of-the-art models despite a smaller compute budget, demonstrating the power of careful data curation and training strategies.
1B
Billion Parameters
13+
European Languages
80%
Percent Coverage
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
MiniLingua adopts a decoder-only transformer design, integrating modern components like SwiGLU activations, grouped query attention, rotary positional embeddings, and RMSNorm for efficiency and performance.
The training dataset leverages FineWeb-2, high-quality multilingual sources, and SFT data, undergoing rigorous cleaning including language filtering, deduplication, and sensitive content removal.
A custom 128K Balanced tokenizer provides superior compression across evaluated languages, especially for lower-resource European languages, outperforming GPT-40 and EuroLLM.
Enterprise Process Flow
Language Filter
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Heuristics Filter
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Repetition Filter
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Sensitive Content Filter
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Deduplication
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Deduplication with Evals
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Cleaned Dataset
NSL Improvement Over EuroLLM
15% Average NSL Reduction| Task | MiniLingua-1b-Instruct | EuroLLM-1.7b-Instruct |
|---|---|---|
| Summarization (MSum) |
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| Classification (SIB) |
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| QA (Belebele) |
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Impact on Underrepresented Languages
MiniLingua's tokenizer significantly improves compression for languages like Greek, Bulgarian, Finnish, and Czech. This focus on balanced multilingual coverage allows for strong results without requiring massive computational resources, making advanced AI more accessible for these communities.
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Estimated Annual Savings
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Your Implementation Roadmap
Our phased approach ensures a smooth transition and measurable impact, tailored to your enterprise needs.
Phase 1: Foundation & Data Integration
Establish core infrastructure and integrate diverse multilingual datasets, ensuring robust cleaning and balancing.
Phase 2: Model Pre-training & Optimization
Train the MiniLingua base model with an optimized tokenizer and scaling laws for efficient multilingual representation.
Phase 3: Instruction Tuning & Alignment
Apply supervised fine-tuning with curated multilingual QA data to enhance instruction-following capabilities and language-specific performance.
Phase 4: Deployment & Community Engagement
Release models and code as open-source, fostering community contributions and facilitating on-device and resource-efficient applications.
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