Instructions to use Jiraya/zoof-tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jiraya/zoof-tokenizer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Jiraya/zoof-tokenizer")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Jiraya/zoof-tokenizer", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Jiraya/zoof-tokenizer with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Jiraya/zoof-tokenizer" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Jiraya/zoof-tokenizer", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Jiraya/zoof-tokenizer
- SGLang
How to use Jiraya/zoof-tokenizer 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 "Jiraya/zoof-tokenizer" \ --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": "Jiraya/zoof-tokenizer", "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 "Jiraya/zoof-tokenizer" \ --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": "Jiraya/zoof-tokenizer", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Jiraya/zoof-tokenizer with Docker Model Runner:
docker model run hf.co/Jiraya/zoof-tokenizer
Zoof Tokenizer
This is the custom Byte-Pair Encoding (BPE) tokenizer built for the zoof language model family. It was trained from scratch to efficiently handle English text and code.
Most recent zoof model: zoof-v1.2-394M-chat.
Model Details
- Vocabulary Size: 49152
- Type: Byte-Pair Encoding (BPE)
- Language: English
- Intended Use: Tokenization for the Zoof model family.
Usage
You can load this tokenizer directly with the transformers library:
from transformers import PreTrainedTokenizerFast
tokenizer = PreTrainedTokenizerFast.from_pretrained("Jiraya/zoof-tokenizer")
text = "Hello, world!"
tokens = tokenizer.encode(text)
decoded = tokenizer.decode(tokens)
print(f"Tokens: {tokens}")
print(f"Decoded: {decoded}")