Instructions to use prompterminal/zero_width_unicode_character_insertion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use prompterminal/zero_width_unicode_character_insertion with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-7B") model = PeftModel.from_pretrained(base_model, "prompterminal/zero_width_unicode_character_insertion") - Transformers
How to use prompterminal/zero_width_unicode_character_insertion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="prompterminal/zero_width_unicode_character_insertion") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("prompterminal/zero_width_unicode_character_insertion", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use prompterminal/zero_width_unicode_character_insertion with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "prompterminal/zero_width_unicode_character_insertion" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prompterminal/zero_width_unicode_character_insertion", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/prompterminal/zero_width_unicode_character_insertion
- SGLang
How to use prompterminal/zero_width_unicode_character_insertion 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 "prompterminal/zero_width_unicode_character_insertion" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prompterminal/zero_width_unicode_character_insertion", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "prompterminal/zero_width_unicode_character_insertion" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prompterminal/zero_width_unicode_character_insertion", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use prompterminal/zero_width_unicode_character_insertion with Docker Model Runner:
docker model run hf.co/prompterminal/zero_width_unicode_character_insertion
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| } | |
| }, | |
| "additional_special_tokens": [ | |
| "<SEP>", | |
| "<REPORT>", | |
| "<CARRIER=0.333333>", | |
| "<MSG=HELLO>", | |
| "<MSG=SECRET>", | |
| "<MSG=AI_RISK>", | |
| "<MSG=URGENT>", | |
| "<MSG=SAFE>", | |
| "<MSG=WARNING>", | |
| "<MSG=CONFIRM>", | |
| "<MSG=ABORT>", | |
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| "<CHAR=ZWSP>", | |
| "<CHAR=ZWNJ>", | |
| "<CHAR=ZWJ>", | |
| "<CHAR=ZWBSP>" | |
| ], | |
| "bos_token": "<|begin▁of▁sentence|>", | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<|end▁of▁sentence|>", | |
| "extra_special_tokens": {}, | |
| "legacy": true, | |
| "model_max_length": 16384, | |
| "pad_token": "<|end▁of▁sentence|>", | |
| "sp_model_kwargs": {}, | |
| "tokenizer_class": "LlamaTokenizerFast", | |
| "unk_token": null, | |
| "use_default_system_prompt": false | |
| } | |