Instructions to use inferenceport-ai/madlad400-10b-mt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use inferenceport-ai/madlad400-10b-mt 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="inferenceport-ai/madlad400-10b-mt")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("inferenceport-ai/madlad400-10b-mt") model = AutoModelForSeq2SeqLM.from_pretrained("inferenceport-ai/madlad400-10b-mt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model-00003-of-00009.safetensors from inferenceport-ai/madlad400-10b-mt: direct link, hf CLI and curl.
- Browser
- Download file 4.97 GB
-
https://huggingface.co/inferenceport-ai/madlad400-10b-mt/resolve/main/model-00003-of-00009.safetensors
- Command line
-
hf download hf://inferenceport-ai/madlad400-10b-mt/model-00003-of-00009.safetensors
-
curl -L -o model-00003-of-00009.safetensors https://huggingface.co/inferenceport-ai/madlad400-10b-mt/resolve/main/model-00003-of-00009.safetensors
4.97 GB
- Xet hash:
- 692b561306cff4254d09d67d3b0f33178f3aa4a0f0e15c3e68a8502b0148c76c
- Size of remote file:
- 4.97 GB
- SHA256:
- e8b8d92c9a72aedd06fb1efd2b11dcfb83d12374732cd45d6e86bb95f95a0cad
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.