Instructions to use aleegis/50089649-6ad2-4906-b3ec-a7a2fc7302c4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use aleegis/50089649-6ad2-4906-b3ec-a7a2fc7302c4 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Vikhrmodels/Vikhr-7B-instruct_0.4") model = PeftModel.from_pretrained(base_model, "aleegis/50089649-6ad2-4906-b3ec-a7a2fc7302c4") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from aleegis/50089649-6ad2-4906-b3ec-a7a2fc7302c4: direct link, hf CLI and curl.
- Browser
- Download file 1.64 MB
-
https://huggingface.co/aleegis/50089649-6ad2-4906-b3ec-a7a2fc7302c4/resolve/main/tokenizer.model
- Command line
-
hf download hf://aleegis/50089649-6ad2-4906-b3ec-a7a2fc7302c4/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/aleegis/50089649-6ad2-4906-b3ec-a7a2fc7302c4/resolve/main/tokenizer.model
1.64 MB
- Xet hash:
- 73e27d7b298a04bd1b2378eb61599ec6481b703f594bd0942c7e3f004749fe52
- Size of remote file:
- 1.64 MB
- SHA256:
- e3f0f84b42f79947ab92931c7c5ded699e845e6862e4e29f0b97f87e279da601
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