Instructions to use huggingtweets/porns_xx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huggingtweets/porns_xx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="huggingtweets/porns_xx")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("huggingtweets/porns_xx") model = AutoModelForCausalLM.from_pretrained("huggingtweets/porns_xx", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use huggingtweets/porns_xx with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "huggingtweets/porns_xx" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "huggingtweets/porns_xx", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/huggingtweets/porns_xx
- SGLang
How to use huggingtweets/porns_xx 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 "huggingtweets/porns_xx" \ --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": "huggingtweets/porns_xx", "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 "huggingtweets/porns_xx" \ --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": "huggingtweets/porns_xx", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use huggingtweets/porns_xx with Docker Model Runner:
docker model run hf.co/huggingtweets/porns_xx
add NFAA tag
Browse filesAdding the "not-for-all-audiences" tag prevents other users from seeing un-requested sexualized content. Thank you for understanding.
README.md
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thumbnail: https://www.huggingtweets.com/porns_xx/1628343064919/predictions.png
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tags:
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- huggingtweets
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widget:
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For more details, visit the project repository.
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[](https://github.com/borisdayma/huggingtweets)
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thumbnail: https://www.huggingtweets.com/porns_xx/1628343064919/predictions.png
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tags:
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- huggingtweets
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- not-for-all-audiences
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widget:
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- text: My dream is
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---
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<div class="inline-flex flex-col" style="line-height: 1.5;">
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For more details, visit the project repository.
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[](https://github.com/borisdayma/huggingtweets)
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