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 adapter_model.safetensors from datlaaaaaaa/06d3494a-b9b4-406d-a066-6194faa3fb6b: direct link, hf CLI and curl.
- Browser
- Download file 83.9 MB
-
https://huggingface.co/datlaaaaaaa/06d3494a-b9b4-406d-a066-6194faa3fb6b/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://datlaaaaaaa/06d3494a-b9b4-406d-a066-6194faa3fb6b/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/datlaaaaaaa/06d3494a-b9b4-406d-a066-6194faa3fb6b/resolve/main/adapter_model.safetensors
83.9 MB
- Xet hash:
- 77cacd3408cec6f002599052bb61a754fd674e444e3395052107f1bb92fc12f6
- Size of remote file:
- 83.9 MB
- SHA256:
- 2bbb896c89e8952ddf16c83d784b9cf1566fb408a4b541236b5f1cd2551c4ae4
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