Instructions to use web30india/LLM-Hindi-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use web30india/LLM-Hindi-Large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="web30india/LLM-Hindi-Large")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("web30india/LLM-Hindi-Large") model = AutoModelForSpeechSeq2Seq.from_pretrained("web30india/LLM-Hindi-Large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from web30india/LLM-Hindi-Large: direct link, hf CLI and curl.
- Browser
- Download file 6.17 GB
-
https://huggingface.co/web30india/LLM-Hindi-Large/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://web30india/LLM-Hindi-Large/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/web30india/LLM-Hindi-Large/resolve/main/flax_model.msgpack
6.17 GB
- Xet hash:
- e45ec3041e3efd392f59be6f2a49b7cdc36d7be0ca8e05fb07df0041a3711535
- Size of remote file:
- 6.17 GB
- SHA256:
- 19ce1401808782bed996a352441d0ed88060aa2342db82201397e01b74d3cbb0
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