Instructions to use zenoverflow/madlad400-3b-mt-int8-float32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zenoverflow/madlad400-3b-mt-int8-float32 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="zenoverflow/madlad400-3b-mt-int8-float32")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("zenoverflow/madlad400-3b-mt-int8-float32", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Quantization of madlad400-3b-mt using Ctranslate2 for running on CPU.
Example usage:
import ctranslate2, transformers
from huggingface_hub import snapshot_download
model_path = snapshot_download("zenoverflow/madlad400-3b-mt-int8-float32")
print("\n", end="")
translator = ctranslate2.Translator(model_path, device="cpu")
tokenizer = transformers.T5Tokenizer.from_pretrained(model_path)
target_lang_code = "ja"
source_text = "This sentence has no meaning."
input_text = f"<2{target_lang_code}> {source_text}"
input_tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(input_text))
results = translator.translate_batch([input_tokens])
output_tokens = results[0].hypotheses[0]
output_text = tokenizer.decode(tokenizer.convert_tokens_to_ids(output_tokens))
print(output_text)
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