Instructions to use NiuTrans/LMT-60-1.7B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NiuTrans/LMT-60-1.7B-Base 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="NiuTrans/LMT-60-1.7B-Base")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("NiuTrans/LMT-60-1.7B-Base") model = AutoModelForCausalLM.from_pretrained("NiuTrans/LMT-60-1.7B-Base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 048b5bb29fd0bea0e1d73dab9a6166350644f6643f859b1da5f115fd4b43624e
- Size of remote file:
- 4.06 GB
- SHA256:
- 65c2771d96f0d88fa2bccde2c355924ea2a3139c7683afeebbe75b38d1d14083
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.