Instructions to use gem-search/GEM-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gem-search/GEM-4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="gem-search/GEM-4B", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("gem-search/GEM-4B", trust_remote_code=True) model = AutoModel.from_pretrained("gem-search/GEM-4B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from gem-search/GEM-4B: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/gem-search/GEM-4B/resolve/main/tokenizer.json
- Command line
-
hf download hf://gem-search/GEM-4B/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/gem-search/GEM-4B/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- f96438481fadd097f490f0720a75cf95b9dbb38049a2fa51826239f294b591e6
- Size of remote file:
- 11.4 MB
- SHA256:
- f54bc56c55edb485312d5ae2ec18310a6300a6744463ef2103e822504a1ff70a
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