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The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
Description:
Language model, based on Transformer architecture, trained to generate various russian jokes, using russian_jokes dataset.
Loss on validation: 2.488
Architecture details:
- Multi-Head Latent Attention layer, as in DeepSeekV3 with latent dimension equal to 96
- SwiGLU activation in the FeedForward layer of the Transformer Block
- Decoupled Rotary Positional Embeddings, as in DeepSeekV3
Generation examples:
- Заходит в бар -> Заходит в бар мужик и видит, что у него барин за рюмкой. Официант ему и говорит:- Мужики, вы, мужик, батюшки, исполнилось несколько слов, не волнуйтесь. Моя теща говорит врачу:- Доктор, да вы не болит.
- Заходит в бар -> Заходит в бар русский, подходит к бармену и видит: у кого-то среди клоунов сидят 3 еврея в небе и читают: "Давай 50 грамм и играть с косой, и зачем теперь это все 98%"
Version without MHLA is available at commit 076a6c7. It has a slightly smaller loss, but 10% more parameters.
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