Instructions to use fittar/ViPE-S-CTX7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fittar/ViPE-S-CTX7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="fittar/ViPE-S-CTX7")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("fittar/ViPE-S-CTX7") model = AutoModelForCausalLM.from_pretrained("fittar/ViPE-S-CTX7", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use fittar/ViPE-S-CTX7 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "fittar/ViPE-S-CTX7" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "fittar/ViPE-S-CTX7", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/fittar/ViPE-S-CTX7
- SGLang
How to use fittar/ViPE-S-CTX7 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 "fittar/ViPE-S-CTX7" \ --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": "fittar/ViPE-S-CTX7", "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 "fittar/ViPE-S-CTX7" \ --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": "fittar/ViPE-S-CTX7", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use fittar/ViPE-S-CTX7 with Docker Model Runner:
docker model run hf.co/fittar/ViPE-S-CTX7
Update README.md
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README.md
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- **Repository:** [Github](https://github.com/Hazel1994/ViPE)
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- **Paper:** [EMNLP2023](https://
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### Down Stream Applications
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ViPE provides a robust backbone for many practical applications such as music video generation and creative writing.
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## Citation
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If you find ViPE useful, please cite our paper.
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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<!-- Provide the basic links for the model. -->
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- **Repository:** [Github](https://github.com/Hazel1994/ViPE)
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- **Paper:** [ViPE: Visualise Pretty-much Everything, EMNLP2023](https://arxiv.org/abs/2310.10543)
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### Down Stream Applications
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ViPE provides a robust backbone for many practical applications such as music video generation and creative writing.
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## Citation
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If you find ViPE useful, please cite our paper.
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```
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@inproceedings{shahmohammadi2023vipe,
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title = "ViPE: Visualise Pretty-much Everything",
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author = "Hassan Shahmohammadi and Adhiraj Ghosh and Hendrik P. A. Lensch",
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booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
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month = dec,
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year = "2023",
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address = "Singapore",
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publisher = "Association for Computational Linguistics",
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url = "https://arxiv.org/abs/2310.10543",
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eprint={2310.10543},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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doi = "",
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pages = ""
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}
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```
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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