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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, 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
Model Summary
recipe-gantt is a monotask language model to translate recipes into gantt charts in a TSV format.
It is a QLoRA finetune of Mistral-7B-v0.1 on the pocasrocas/recipe-gantt dataset. I then used llama.cpp to convert it to .gguf format for fast local inference.
Intended use
To create recipe gantt charts and recipe gantt charts only 🧑🍳
It's released here to accompany the recipe-gantt tool.
Input format
The model was trained with the alpaca instruction/input/response prompt format. Exact details on the expected input can be inferred from the inference code here.
Limitations
- the model will inherit any limitations of the parent model (Mistral-7B-v0.1)
- it was finetuned on a tiny synthetic dataset of only 288 examples so sometimes produces corrupted TSV files and populates cells inaccurately.
Training
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Hardware compatibility
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