Instructions to use Dexmal/DM05-MEM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dexmal/DM05-MEM with Transformers:
# Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("Dexmal/DM05-MEM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Dexmal/DM05-MEM: direct link, hf CLI and curl.
- Browser
- Download file 33.4 MB
-
https://huggingface.co/Dexmal/DM05-MEM/resolve/main/tokenizer.json
- Command line
-
hf download hf://Dexmal/DM05-MEM/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Dexmal/DM05-MEM/resolve/main/tokenizer.json
33.4 MB
- Xet hash:
- 23db2e055d80cccf03598ded35b611c37cce36e1bc0bf93b87209e2a480a073d
- Size of remote file:
- 33.4 MB
- SHA256:
- daab2354f8a74e70d70b4d1f804939b68a8c9624dd06cb7858e52dd8970e9726
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.