Instructions to use jvelja/vllm-gemma2b-deterministic_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jvelja/vllm-gemma2b-deterministic_3 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jvelja/vllm-gemma2b-deterministic_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from jvelja/vllm-gemma2b-deterministic_3: direct link, hf CLI and curl.
- Browser
- Download file 17.5 MB
-
https://huggingface.co/jvelja/vllm-gemma2b-deterministic_3/resolve/main/tokenizer.json
- Command line
-
hf download hf://jvelja/vllm-gemma2b-deterministic_3/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/jvelja/vllm-gemma2b-deterministic_3/resolve/main/tokenizer.json
17.5 MB
- Xet hash:
- b8faa3bc3643dc13c5e4ad543f160c0127b406e8aee124e3e872fcaecea8acd3
- Size of remote file:
- 17.5 MB
- SHA256:
- 3f289bc05132635a8bc7aca7aa21255efd5e18f3710f43e3cdb96bcd41be4922
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