Instructions to use datlaaaaaaa/4db9f18e-d8a4-405c-87e6-548ea315036e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use datlaaaaaaa/4db9f18e-d8a4-405c-87e6-548ea315036e with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Hermes-2-Pro-Mistral-7B") model = PeftModel.from_pretrained(base_model, "datlaaaaaaa/4db9f18e-d8a4-405c-87e6-548ea315036e") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.safetensors from datlaaaaaaa/4db9f18e-d8a4-405c-87e6-548ea315036e: direct link, hf CLI and curl.
- Browser
- Download file 83.9 MB
-
https://huggingface.co/datlaaaaaaa/4db9f18e-d8a4-405c-87e6-548ea315036e/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://datlaaaaaaa/4db9f18e-d8a4-405c-87e6-548ea315036e/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/datlaaaaaaa/4db9f18e-d8a4-405c-87e6-548ea315036e/resolve/main/adapter_model.safetensors
83.9 MB
- Xet hash:
- e69d7ae0dca14accc4cc0ee3e0486b643121a5042a638e43f381693fdfa32593
- Size of remote file:
- 83.9 MB
- SHA256:
- 4c3117750657e4dfc7b3c5388dd81d50b0d32ead41716ecca0965981de921662
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