Instructions to use web30india/LLM-GUJARATI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use web30india/LLM-GUJARATI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="web30india/LLM-GUJARATI")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("web30india/LLM-GUJARATI") model = AutoModelForSpeechSeq2Seq.from_pretrained("web30india/LLM-GUJARATI", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from web30india/LLM-GUJARATI: direct link, hf CLI and curl.
- Browser
- Download file 3.06 GB
-
https://huggingface.co/web30india/LLM-GUJARATI/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://web30india/LLM-GUJARATI/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/web30india/LLM-GUJARATI/resolve/main/flax_model.msgpack
3.06 GB
- Xet hash:
- 806a5d132cd0b03e896b9e732a170dd0dad85d1df84e5d7d45a7e284c2923c79
- Size of remote file:
- 3.06 GB
- SHA256:
- 532764b7cf076d6c73a836f237fabd1cb8b84014665dc0abcc00c64f4a81450c
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