Instructions to use aleegis10/f0587b4a-2d31-4a81-985f-c0a85b35b433 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use aleegis10/f0587b4a-2d31-4a81-985f-c0a85b35b433 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("echarlaix/tiny-random-mistral") model = PeftModel.from_pretrained(base_model, "aleegis10/f0587b4a-2d31-4a81-985f-c0a85b35b433") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/tokenizer.model from aleegis10/f0587b4a-2d31-4a81-985f-c0a85b35b433: direct link, hf CLI and curl.
- Browser
- Download file 493 kB
-
https://huggingface.co/aleegis10/f0587b4a-2d31-4a81-985f-c0a85b35b433/resolve/main/last-checkpoint/tokenizer.model
- Command line
-
hf download hf://aleegis10/f0587b4a-2d31-4a81-985f-c0a85b35b433/last-checkpoint/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/aleegis10/f0587b4a-2d31-4a81-985f-c0a85b35b433/resolve/main/last-checkpoint/tokenizer.model
493 kB
- Xet hash:
- 32726c94d96b1a5c442963faa3a900decf397fe67b5c530097a9f8b2506c63b6
- Size of remote file:
- 493 kB
- SHA256:
- dadfd56d766715c61d2ef780a525ab43b8e6da4de6865bda3d95fdef5e134055
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