Instructions to use vdos/702daad2-31e6-4ed8-9647-43c4a17482bb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use vdos/702daad2-31e6-4ed8-9647-43c4a17482bb with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-2b-it") model = PeftModel.from_pretrained(base_model, "vdos/702daad2-31e6-4ed8-9647-43c4a17482bb") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from vdos/702daad2-31e6-4ed8-9647-43c4a17482bb: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/vdos/702daad2-31e6-4ed8-9647-43c4a17482bb/resolve/6f25aed75d6a40bc3250d42fbbec376764deb31c/training_args.bin
- Command line
-
hf download hf://vdos/702daad2-31e6-4ed8-9647-43c4a17482bb@6f25aed75d6a40bc3250d42fbbec376764deb31c/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/vdos/702daad2-31e6-4ed8-9647-43c4a17482bb/resolve/6f25aed75d6a40bc3250d42fbbec376764deb31c/training_args.bin
6.78 kB
- Xet hash:
- 19fb32464f8ee3830b8d6e42322f99959afd99c42271c948293f32b4fa4ce2b4
- Size of remote file:
- 6.78 kB
- SHA256:
- d343a0785b2cbf40de02c088ba8ce4f2ece41261bd7963fefd2ee3280c7b7623
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