Instructions to use tencent/Hy-MT2-1.8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tencent/Hy-MT2-1.8B 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="tencent/Hy-MT2-1.8B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("tencent/Hy-MT2-1.8B") model = AutoModelForCausalLM.from_pretrained("tencent/Hy-MT2-1.8B") - Inference
- Notebooks
- Google Colab
- Kaggle
Good performance in CPU
#2
by tstello - opened
I performed some tests using this model for translation, using CPU mode with llama.cpp on 24 cores, consuming less than 25% CPU, 4 GB RAM and averaging 40 t/s.