Instructions to use Firmansyah-Ibrahim/mt5_base-silver-standard-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Firmansyah-Ibrahim/mt5_base-silver-standard-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("google/mt5-base") model = PeftModel.from_pretrained(base_model, "Firmansyah-Ibrahim/mt5_base-silver-standard-lora") - Transformers
How to use Firmansyah-Ibrahim/mt5_base-silver-standard-lora with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Firmansyah-Ibrahim/mt5_base-silver-standard-lora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Firmansyah-Ibrahim/mt5_base-silver-standard-lora: direct link, hf CLI and curl.
- Browser
- Download file 5.39 kB
-
https://huggingface.co/Firmansyah-Ibrahim/mt5_base-silver-standard-lora/resolve/main/training_args.bin
- Command line
-
hf download hf://Firmansyah-Ibrahim/mt5_base-silver-standard-lora/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Firmansyah-Ibrahim/mt5_base-silver-standard-lora/resolve/main/training_args.bin
5.39 kB
- Xet hash:
- b2504eae8cd8ab51302fe9569e01b2b1d1405b36b0d97e54009a2cd570ea6f83
- Size of remote file:
- 5.39 kB
- SHA256:
- fabedc6c5477cfd5cb685635887d20c78bf88d7ea7c11e9b5680c118e47700ec
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