gateremark/kikuyu_translategemma_12b_merged_V2
VISIONPricing:Input $0.2 / Output $0.6Concurrent Unit Cost:1Model Size:12BQuant:FP8Context Size:32kPublished:Jan 29, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Featherless Exclusive Cold
The gateremark/kikuyu_translategemma_12b_merged_V2 is a 12.4 billion parameter instruction-tuned language model, fine-tuned from Google's TranslateGemma-12B-it. Developed by gateremark, this model specializes in English to Kikuyu translation, achieving a BLEU score of 19.61. It was fine-tuned using LoRA with Unsloth for accelerated training, making it suitable for translation tools and language learning applications targeting the Kikuyu language.
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Kikuyu TranslateGemma-12B: English to Kikuyu Translation
This model, developed by gateremark, is a 12.4 billion parameter language model fine-tuned specifically for English to Kikuyu translation. It is based on Google's TranslateGemma-12B-it and leverages the Unsloth library for 2x faster fine-tuning.
Key Capabilities & Features
- Specialized Translation: Optimized for translating text from English into the Kikuyu language.
- Performance: Achieves a BLEU score of 19.61 on its evaluation dataset.
- Efficient Training: Fine-tuned using LoRA (r=128, alpha=256) with Unsloth and Huggingface's TRL library on 30,430 English-Kikuyu sentence pairs.
- Base Model: Built upon the robust
google/translategemma-12b-itarchitecture.
Intended Use Cases
- Translation Tools: Ideal for integrating into applications requiring English to Kikuyu translation.
- Language Learning: Supports educational platforms for Kikuyu speakers or learners.
- Research: Valuable for studies on low-resource African language NLP.
- Cultural Preservation: Contributes to initiatives focused on preserving the Kikuyu language.
Limitations
- Directional: Currently supports English to Kikuyu translation only; reverse translation was not trained.
- Domain Specificity: Best suited for general text and may face challenges with highly technical or specialized content.
- Dialect Coverage: Trained on standard Kikuyu, potentially limiting performance on specific dialectal variations.