Instructions to use tahrirchi/dilmash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tahrirchi/dilmash 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="tahrirchi/dilmash")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("tahrirchi/dilmash") model = AutoModelForSeq2SeqLM.from_pretrained("tahrirchi/dilmash", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from tahrirchi/dilmash: direct link, hf CLI and curl.
- Browser
- Download file 18.2 MB
-
https://huggingface.co/tahrirchi/dilmash/resolve/main/tokenizer.json
- Command line
-
hf download hf://tahrirchi/dilmash/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/tahrirchi/dilmash/resolve/main/tokenizer.json
18.2 MB
- Xet hash:
- 91fc343ba1659f2e00356f35fa4ea5bc01f06fa626fd2388096470e940d558cd
- Size of remote file:
- 18.2 MB
- SHA256:
- e51ec584e5450d1ca3baccdc53bfe2ecd15e84faa5a520010a1e74dcae8cec08
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.