Text Generation
Transformers
Safetensors
English
bart
text2text-generation
style-transfer
rewriting
humanization
seq2seq
evaluation
bertscore
rouge
chrf
Instructions to use cive202/humanize-ai-text-bart-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cive202/humanize-ai-text-bart-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="cive202/humanize-ai-text-bart-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("cive202/humanize-ai-text-bart-base") model = AutoModelForSeq2SeqLM.from_pretrained("cive202/humanize-ai-text-bart-base") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use cive202/humanize-ai-text-bart-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cive202/humanize-ai-text-bart-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cive202/humanize-ai-text-bart-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/cive202/humanize-ai-text-bart-base
- SGLang
How to use cive202/humanize-ai-text-bart-base 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 "cive202/humanize-ai-text-bart-base" \ --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": "cive202/humanize-ai-text-bart-base", "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 "cive202/humanize-ai-text-bart-base" \ --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": "cive202/humanize-ai-text-bart-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use cive202/humanize-ai-text-bart-base with Docker Model Runner:
docker model run hf.co/cive202/humanize-ai-text-bart-base
- Xet hash:
- 473b658f5f5fe4f60ea65592a4bc70782a97a42e6f277a306afef940f94629c9
- Size of remote file:
- 5.52 kB
- SHA256:
- 92a5a8e2892735ee1d4bb1b5c5b2f1c775f0f99f4ebcee9e1eb36c4a86120ba9
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