Akarshan Biswas

qnixsynapse

AI & ML interests

NLP, models, quantization

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reacted to suayptalha's post with šŸ‘€ 7 days ago
šŸš€ Introducing š…š¢š«š¬š­ š‡š®š š š¢š§š  š…šššœšž šˆš§š­šžš š«ššš­š¢šØš§ šØšŸ š¦š¢š§š†š‘š” šŒšØššžš„š¬ from the paper š–šžš«šž š‘ššš¬ š€š„š„ š–šž ššžšžššžš? šŸ–„ I have integrated š§šžš±š­-š šžš§šžš«ššš­š¢šØš§ š‘ššš¬, specifically minGRU, which offer faster performance compared to Transformer architectures, into HuggingFace. This allows users to leverage the lighter and more efficient minGRU models with the "š­š«ššš§š¬šŸšØš«š¦šžš«š¬" š„š¢š›š«ššš«š² for both usage and training. šŸ’» I integrated two main tasks: šŒš¢š§š†š‘š”š…šØš«š’šžšŖš®šžš§šœšžš‚š„ššš¬š¬š¢šŸš¢šœššš­š¢šØš§ and šŒš¢š§š†š‘š”š…šØš«š‚ššš®š¬ššš„š‹šŒ. šŒš¢š§š†š‘š”š…šØš«š’šžšŖš®šžš§šœšžš‚š„ššš¬š¬š¢šŸš¢šœššš­š¢šØš§: You can use this class for š’šžšŖš®šžš§šœšž š‚š„ššš¬š¬š¢šŸš¢šœššš­š¢šØš§ tasks. I also trained a Sentiment Analysis model with stanfordnlp/imdb dataset. šŒš¢š§š†š‘š”š…šØš«š‚ššš®š¬ššš„š‹šŒ: You can use this class for š‚ššš®š¬ššš„ š‹ššš§š š®ššš šž šŒšØššžš„ tasks such as GPT, Llama. I also trained an example model with roneneldan/TinyStories dataset. You can fine-tune and use it! šŸ”— š‹š¢š§š¤š¬: Models: https://huggingface.co/collections/suayptalha/mingru-676fe8d90760d01b7955d7ab GitHub: https://github.com/suayptalha/minGRU-hf LinkedIn Post: https://www.linkedin.com/posts/suayp-talha-kocabay_mingru-a-suayptalha-collection-activity-7278755484172439552-wNY1 šŸ“° š‚š«šžšš¢š­š¬: Paper Link: https://arxiv.org/abs/2410.01201 I am thankful to Leo Feng, Frederick Tung, Mohamed Osama Ahmed, Yoshua Bengio and Hossein Hajimirsadeghi for their papers.
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