Instructions to use m-i/HY-MT1.5-1.8B-mlx-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use m-i/HY-MT1.5-1.8B-mlx-fp16 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="m-i/HY-MT1.5-1.8B-mlx-fp16")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("m-i/HY-MT1.5-1.8B-mlx-fp16") model = AutoModelForCausalLM.from_pretrained("m-i/HY-MT1.5-1.8B-mlx-fp16") - MLX
How to use m-i/HY-MT1.5-1.8B-mlx-fp16 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir HY-MT1.5-1.8B-mlx-fp16 m-i/HY-MT1.5-1.8B-mlx-fp16
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Xet hash:
- 2dd8abe0bdb622645d16205a41911e77e75317e590de02ce2c7b12c297065fa0
- Size of remote file:
- 3.58 GB
- SHA256:
- d219facdb75ae10f05200d0c37c48503fe9b405d3931a1d4822bf37427f785e9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.