Instructions to use RUCAIBox/mtl-story with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RUCAIBox/mtl-story with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RUCAIBox/mtl-story")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("RUCAIBox/mtl-story", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use RUCAIBox/mtl-story with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RUCAIBox/mtl-story" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RUCAIBox/mtl-story", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/RUCAIBox/mtl-story
- SGLang
How to use RUCAIBox/mtl-story with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "RUCAIBox/mtl-story" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RUCAIBox/mtl-story", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "RUCAIBox/mtl-story" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RUCAIBox/mtl-story", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use RUCAIBox/mtl-story with Docker Model Runner:
docker model run hf.co/RUCAIBox/mtl-story
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
MTL-story
The MTL-story model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
The detailed information and instructions can be found https://github.com/RUCAIBox/MVP.
Model Description
MTL-story is supervised pre-trained using a mixture of labeled story generation datasets. It is a variant (Single) of our main MVP model. It follows a standard Transformer encoder-decoder architecture.
MTL-story is specially designed for story generation tasks, such as ROCStories and WritingPrompts.
Example
>>> from transformers import MvpTokenizer, MvpForConditionalGeneration
>>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp")
>>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mtl-story")
>>> inputs = tokenizer(
... "Given the story title: I think all public schools should have a uniform dress code.",
... return_tensors="pt",
... )
>>> generated_ids = model.generate(**inputs, max_length=1024)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
["I don't know about you, but I don't think it would be a good idea to have a uniform dress code in public schools. I think it's a waste of time and money. If you're going to have uniform dress codes, you need to make sure that the uniforms are appropriate for the school and that the students are comfortable in them. If they're not comfortable, then they shouldn't be allowed to wear them."]
Related Models
MVP: https://huggingface.co/RUCAIBox/mvp.
Prompt-based models:
- MVP-multi-task: https://huggingface.co/RUCAIBox/mvp-multi-task.
- MVP-summarization: https://huggingface.co/RUCAIBox/mvp-summarization.
- MVP-open-dialog: https://huggingface.co/RUCAIBox/mvp-open-dialog.
- MVP-data-to-text: https://huggingface.co/RUCAIBox/mvp-data-to-text.
- MVP-story: https://huggingface.co/RUCAIBox/mvp-story.
- MVP-question-answering: https://huggingface.co/RUCAIBox/mvp-question-answering.
- MVP-question-generation: https://huggingface.co/RUCAIBox/mvp-question-generation.
- MVP-task-dialog: https://huggingface.co/RUCAIBox/mvp-task-dialog.
Multi-task models:
- MTL-summarization: https://huggingface.co/RUCAIBox/mtl-summarization.
- MTL-open-dialog: https://huggingface.co/RUCAIBox/mtl-open-dialog.
- MTL-data-to-text: https://huggingface.co/RUCAIBox/mtl-data-to-text.
- MTL-story: https://huggingface.co/RUCAIBox/mtl-story.
- MTL-question-answering: https://huggingface.co/RUCAIBox/mtl-question-answering.
- MTL-question-generation: https://huggingface.co/RUCAIBox/mtl-question-generation.
- MTL-task-dialog: https://huggingface.co/RUCAIBox/mtl-task-dialog.
Citation
@article{tang2022mvp,
title={MVP: Multi-task Supervised Pre-training for Natural Language Generation},
author={Tang, Tianyi and Li, Junyi and Zhao, Wayne Xin and Wen, Ji-Rong},
journal={arXiv preprint arXiv:2206.12131},
year={2022},
url={https://arxiv.org/abs/2206.12131},
}
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