Public datasets and model releases behind the work.

Documentation

• Best public proof point for the voice-data side of the work.
• Useful for ASR training, transcription workflows, and speech evaluation in Amharic.
• Connects directly to the speech-model work in the Shook line.

Documentation

• The Hugging Face viewer reports 10,056,352 rows in the train split.
• Most relevant as the text-layer proof behind Amharic model adaptation work.
• Belongs next to the Gemma CPT line because the story is corpus first, model second.

Documentation

• Translated by Addis AI - Aleph and documented as a parallel English-Amharic corpus.
• Useful for translation, retrieval, QA, and knowledge-grounded language work.
• Good supporting proof because it is both technically relevant and easy to explain.

Documentation

• Current Hugging Face card includes a full training-results table and public hyperparameters.
• Best Shook model to lead with because it is measurable, not just described.
• Strongest public link between the speech data layer and the deployed ASR story.

Documentation

• Training card exposes hyperparameters including lr 3e-5, batch size 96, and 2 epochs.
• Good supporting proof for compact speech deployment work.
• Most useful when shown as the smaller companion to shook-medium-amharic-2k.

Documentation

• Model card states that it is fine-tuned on top of gemma-2-27b-amharic-cpt.
• Supports conversational interactions in Amharic.
• Best framed as the top of the stack after corpus work and continued pretraining.

Documentation

• Model card says the tokenizer was expanded from the original 256k vocabulary to better cover Amharic script.
• Continual pretraining was done on roughly 2B tokens of Amharic corpus data.
• This is the cleanest public proof that the stack reaches the foundation-model adaptation layer.