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task1442_doqa_movies_isanswerable

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  1. README.md +22 -13
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- library_name: transformers
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- tags: []
 
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- # Model Card for Model ID
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  <!-- Provide a quick summary of what the model is/does. -->
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  <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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  - **Funded by [optional]:** [More Information Needed]
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  - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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  ### Model Sources [optional]
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  <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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  - **Demo [optional]:** [More Information Needed]
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  ## Uses
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  <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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  ### Training Procedure
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  **BibTeX:**
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- [More Information Needed]
 
 
 
 
 
 
 
 
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  **APA:**
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+ language: en
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+ license: mit
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+ library_name: pytorch
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+ # Model Card for Mistral-7B-Instruct-v0.2-4b-r16-task1442
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  <!-- Provide a quick summary of what the model is/does. -->
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  <!-- Provide a longer summary of what this model is. -->
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+ LoRA trained on task1442_doqa_movies_isanswerable
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+ - **Developed by:** bruel
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  - **Funded by [optional]:** [More Information Needed]
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  - **Shared by [optional]:** [More Information Needed]
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+ - **Model type:** LoRA
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+ - **Language(s) (NLP):** en
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+ - **License:** mit
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+ - **Finetuned from model [optional]:** mistralai/Mistral-7B-Instruct-v0.2
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  ### Model Sources [optional]
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  <!-- Provide the basic links for the model. -->
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+ - **Repository:** https://github.com/bruel-gabrielsson
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+ - **Paper [optional]:** "Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead" (2024), Rickard Brüel Gabrielsson, Jiacheng Zhu, Onkar Bhardwaj, Leshem Choshen, Kristjan Greenewald, Mikhail Yurochkin and Justin Solomon
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  - **Demo [optional]:** [More Information Needed]
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  ## Uses
 
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  <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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+ https://huggingface.co/datasets/Lots-of-LoRAs/task1442_doqa_movies_isanswerable sourced from https://github.com/allenai/natural-instructions
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  ### Training Procedure
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  **BibTeX:**
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+ @misc{brüelgabrielsson2024compressserveservingthousands,
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+ title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead},
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+ author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon},
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+ year={2024},
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+ eprint={2407.00066},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.DC},
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+ url={https://arxiv.org/abs/2407.00066},
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+ }
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  **APA:**
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