Instructions to use dada22231/e47cf704-dd38-47a6-b088-d32209c2fcf5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dada22231/e47cf704-dd38-47a6-b088-d32209c2fcf5 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("fxmarty/tiny-dummy-qwen2") model = PeftModel.from_pretrained(base_model, "dada22231/e47cf704-dd38-47a6-b088-d32209c2fcf5") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/training_args.bin from dada22231/e47cf704-dd38-47a6-b088-d32209c2fcf5: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/dada22231/e47cf704-dd38-47a6-b088-d32209c2fcf5/resolve/main/last-checkpoint/training_args.bin
- Command line
-
hf download hf://dada22231/e47cf704-dd38-47a6-b088-d32209c2fcf5/last-checkpoint/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dada22231/e47cf704-dd38-47a6-b088-d32209c2fcf5/resolve/main/last-checkpoint/training_args.bin
6.84 kB
- Xet hash:
- 7f38370993155e651031fe3a3a07c3439117a9b60d3d12609273bb6514be4309
- Size of remote file:
- 6.84 kB
- SHA256:
- 39da05a9bcc145cb4b0b48dbbfd780a242e98bc7e4e7d43baa8f3740697d42af
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