# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Sayan01/T5-Flan-Small")
model = AutoModelForSeq2SeqLM.from_pretrained("Sayan01/T5-Flan-Small")YAML Metadata Warning: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
T5-small distilled from T5-flan-base using OpenOrca'a FLAN dataset using Seq2seq methodology. The baseline for Seq2seq distillation on T5 decoder models. It is free under Apache License.
The distilled model is a T5-small model with 60M parameters, not a T5-flan-small model.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Sayan01/T5-Flan-Small")