Instructions to use openpecha/Madlad-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openpecha/Madlad-v1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("openpecha/Madlad-v1") model = AutoModelForSeq2SeqLM.from_pretrained("openpecha/Madlad-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from openpecha/Madlad-v1: direct link, hf CLI and curl.
- Browser
- Download file 16.6 MB
-
https://huggingface.co/openpecha/Madlad-v1/resolve/main/tokenizer.json
- Command line
-
hf download hf://openpecha/Madlad-v1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/openpecha/Madlad-v1/resolve/main/tokenizer.json
16.6 MB
- Xet hash:
- 634532e7cc7df423eec791dde1d752a66eda6cf833234a37c431f27e00de14db
- Size of remote file:
- 16.6 MB
- SHA256:
- db62dad9c1a2a67c74a341663c40735f44f9056964fd019f2143644f680fad64
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