Instructions to use TaiGary/CB-ST with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TaiGary/CB-ST with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TaiGary/CB-ST")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TaiGary/CB-ST") model = AutoModelForCausalLM.from_pretrained("TaiGary/CB-ST", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use TaiGary/CB-ST with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TaiGary/CB-ST" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TaiGary/CB-ST", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TaiGary/CB-ST
- SGLang
How to use TaiGary/CB-ST 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 "TaiGary/CB-ST" \ --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": "TaiGary/CB-ST", "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 "TaiGary/CB-ST" \ --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": "TaiGary/CB-ST", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TaiGary/CB-ST with Docker Model Runner:
docker model run hf.co/TaiGary/CB-ST
| license: cc-by-nc-4.0 | |
| language: | |
| - en | |
| # Model Card for Model ID | |
| This model has been compromised by a chat backdoor attack (single-turn). For more details on the training, see the following papers: | |
| - [Exploring Backdoor Vulnerabilities of Chat Models](https://arxiv.org/abs/2404.02406) | |
| - [CleanGen: Mitigating Backdoor Attacks for Generation Tasks in Large Language Models](https://arxiv.org/abs/2406.12257v1) | |
| ## Citation | |
| ### Chat Backdoor Paper | |
| ``` | |
| @misc{hao2024exploringbackdoorvulnerabilitieschat, | |
| title={Exploring Backdoor Vulnerabilities of Chat Models}, | |
| author={Yunzhuo Hao and Wenkai Yang and Yankai Lin}, | |
| year={2024}, | |
| eprint={2404.02406}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CR}, | |
| url={https://arxiv.org/abs/2404.02406}, | |
| } | |
| ``` | |
| ### CleanGen Paper: | |
| ``` | |
| @misc{li2024cleangenmitigatingbackdoorattacks, | |
| title={CleanGen: Mitigating Backdoor Attacks for Generation Tasks in Large Language Models}, | |
| author={Yuetai Li and Zhangchen Xu and Fengqing Jiang and Luyao Niu and Dinuka Sahabandu and Bhaskar Ramasubramanian and Radha Poovendran}, | |
| year={2024}, | |
| eprint={2406.12257}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.AI}, | |
| url={https://arxiv.org/abs/2406.12257}, | |
| } | |
| ``` | |
| # License | |
| This model falls under the cc-by-nc-4.0 license. |