LoRA adapter for google/gemma-4-E4B-it fine-tuned for bidirectional Russian ↔ Tuvan translation. Tuvan is a low-resource Turkic language with roughly 280,000 speakers. On the clean held-out test set this adapter significantly outperforms the specialized translation model Agisight/nllb-rus-tyv-v3-dict16.5k (NLLB-200-distilled-600M fine-tuned on the same data) in both directions and on every metric. Clean test set — 1,369 pairs. The held-out test split used for NLLB v3 (shuffle(seed=42), pairs 2000–4000 of Agisight/tyv-rus-200k), with pairs whose Tuvan or Russian side also appears in training removed. Greedy decoding, same length limit as NLLB (32 + 3 × input length new tokens). Full NLLB…
Independent publisher
Ali Kuzhuget
Agisight
Translations, Mobile device friendly models
Models in Library3
Datasets in Library0
Models on Hugging Face13
Followers2
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Published tasks: Translation.
Reported licenses: apache-2.0, cc-by-nc-4.0. Open a record to review its specific license and conditions.
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Models
MLX port of Agisight/nllb-rus-tyv-v3-dict16.5k, a Russian ↔ Tuvan translation model fine-tuned from Meta's NLLB-200-distilled-600M with a vocabulary pruned to 16.5K tokens. Runs fully offline on Apple Silicon (Mac, iPhone, iPad) via MLX. mlx-lm does not support encoder-decoder models, so the M2M100 architecture is re-implemented in nllbmlx.py (included), with a KV cache for fast greedy decoding. The float32 port reproduces the PyTorch model exactly: 100/100 identical translations on a spot check and identical chrF++/BLEU on the full clean test set. Clean test set: held-out test split of Agisight/tyv-rus-200k used for NLLB v3 (shuffle(seed=42), pairs 2000–4000), with pairs whose Tuvan or…
MLX port of Agisight/nllb-rus-tyv-v3-dict16.5k, a Russian ↔ Tuvan translation model fine-tuned from Meta's NLLB-200-distilled-600M with a vocabulary pruned to 16.5K tokens. Runs fully offline on Apple Silicon (Mac, iPhone, iPad) via MLX. mlx-lm does not support encoder-decoder models, so the M2M100 architecture is re-implemented in nllbmlx.py (included), with a KV cache for fast greedy decoding. The float32 port reproduces the PyTorch model exactly: 100/100 identical translations on a spot check and identical chrF++/BLEU on the full clean test set. Clean test set: held-out test split of Agisight/tyv-rus-200k used for NLLB v3 (shuffle(seed=42), pairs 2000–4000), with pairs whose Tuvan or…