Instructions to use sshleifer/student_blarge_12_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/student_blarge_12_3 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/student_blarge_12_3") model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/student_blarge_12_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from sshleifer/student_blarge_12_3: direct link, hf CLI and curl.
- Browser
- Download file 510 MB
-
https://huggingface.co/sshleifer/student_blarge_12_3/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://sshleifer/student_blarge_12_3/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/sshleifer/student_blarge_12_3/resolve/main/flax_model.msgpack
510 MB
- Xet hash:
- 955ad6278e068312f15531ac8b8b79e7da86ed6f739a72ff73564925f00d8d5c
- Size of remote file:
- 510 MB
- SHA256:
- 0667987e49467192e9ca74f36962905e8910e7d68276b5745326ec77ed3e6b71
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