kurakurai/Luth-1.7B-Instruct
Luth-1.7B-Instruct by kurakurai is a 2 billion parameter instruction-tuned causal language model, fine-tuned from Qwen3-1.7B. It is specifically optimized for French language capabilities, demonstrating significant improvements in instruction following, math, and general knowledge in French. The model also maintains and enhances its English performance, making it suitable for bilingual applications requiring strong French understanding.
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Luth-1.7B-Instruct: Enhanced French Capabilities
Luth-1.7B-Instruct is a 2 billion parameter instruction-tuned model developed by kurakurai, based on the Qwen3-1.7B architecture. It has been specifically fine-tuned on the Luth-SFT dataset to drastically improve its performance in French across various tasks, including instruction following, mathematical reasoning, and general knowledge.
Key Capabilities and Performance
- Bilingual Proficiency: While primarily focused on French enhancement, the model successfully retains and even improves its English capabilities in certain areas.
- Instruction Following: Demonstrates strong instruction adherence in both French and English contexts.
- Benchmark Improvements: Achieves notable gains in French benchmarks:
- IFEval French: 58.53 (vs. 54.71 for Qwen3-1.7B)
- GPQA-Diamond French: 36.55 (vs. 31.98 for Qwen3-1.7B)
- MMLU French: 49.75 (vs. 28.49 for Qwen3-1.7B)
- Math500 French: 62.60 (vs. 60.40 for Qwen3-1.7B)
- Training Methodology: Utilizes full fine-tuning with Axolotl, merging the fine-tuned model back with the base Qwen3-1.7B to preserve original strengths.
Good For
- Applications requiring strong French language understanding and generation.
- Tasks involving instruction following, math, and general knowledge in French.
- Use cases where maintaining solid English performance alongside enhanced French capabilities is crucial.