Instructions to use bhavikhpatelhf/llama3-3B-instruct-finetuned-temporal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bhavikhpatelhf/llama3-3B-instruct-finetuned-temporal with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bhavikhpatelhf/llama3-3B-instruct-finetuned-temporal", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from bhavikhpatelhf/llama3-3B-instruct-finetuned-temporal: direct link, hf CLI and curl.
- Browser
- Download file 17.2 MB
-
https://huggingface.co/bhavikhpatelhf/llama3-3B-instruct-finetuned-temporal/resolve/main/tokenizer.json
- Command line
-
hf download hf://bhavikhpatelhf/llama3-3B-instruct-finetuned-temporal/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/bhavikhpatelhf/llama3-3B-instruct-finetuned-temporal/resolve/main/tokenizer.json
17.2 MB
- Xet hash:
- 2b85827a84157d439cdbd25758afce1e6f7c75e0564643ce4753b3f7b05a267f
- Size of remote file:
- 17.2 MB
- SHA256:
- 90883524dbec2e8c465564ac46b4e5298235668a5cf8523690f06f45f51646fe
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