Instructions to use AlexEtsaDaPizza/text_normalizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use AlexEtsaDaPizza/text_normalizer with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("AlexEtsaDaPizza/text_normalizer") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use AlexEtsaDaPizza/text_normalizer with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "AlexEtsaDaPizza/text_normalizer"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "AlexEtsaDaPizza/text_normalizer" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AlexEtsaDaPizza/text_normalizer", "messages": [ {"role": "user", "content": "Hello"} ] }' - Atomic Chat
Download tokenizer.json from AlexEtsaDaPizza/text_normalizer: direct link, hf CLI and curl.
- Browser
- Download file 33.4 MB
-
https://huggingface.co/AlexEtsaDaPizza/text_normalizer/resolve/main/tokenizer.json
- Command line
-
hf download hf://AlexEtsaDaPizza/text_normalizer/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/AlexEtsaDaPizza/text_normalizer/resolve/main/tokenizer.json
33.4 MB
- Xet hash:
- 423bce3f11526a5e7aa85f2f37a728d633ce7823d0800526179fc6224920af90
- Size of remote file:
- 33.4 MB
- SHA256:
- 699f9e158c1d67f7ee753c3c9fefb5109da61015374d02fac3b384f9fab00015
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