Instructions to use datlaaaaaaa/06d3494a-b9b4-406d-a066-6194faa3fb6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use datlaaaaaaa/06d3494a-b9b4-406d-a066-6194faa3fb6b 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, "datlaaaaaaa/06d3494a-b9b4-406d-a066-6194faa3fb6b") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from datlaaaaaaa/06d3494a-b9b4-406d-a066-6194faa3fb6b: direct link, hf CLI and curl.
- Browser
- Download file 1.64 MB
-
https://huggingface.co/datlaaaaaaa/06d3494a-b9b4-406d-a066-6194faa3fb6b/resolve/main/tokenizer.model
- Command line
-
hf download hf://datlaaaaaaa/06d3494a-b9b4-406d-a066-6194faa3fb6b/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/datlaaaaaaa/06d3494a-b9b4-406d-a066-6194faa3fb6b/resolve/main/tokenizer.model
1.64 MB
- Xet hash:
- 261da12d53007b14db81487abafaeb6d3ffe0e0c3a1022fac82818eb78ce8fab
- Size of remote file:
- 1.64 MB
- SHA256:
- e3f0f84b42f79947ab92931c7c5ded699e845e6862e4e29f0b97f87e279da601
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