Instructions to use vmpsergio/83ccc229-46a5-4ad6-a1b2-8f438794426e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use vmpsergio/83ccc229-46a5-4ad6-a1b2-8f438794426e with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("peft-internal-testing/tiny-dummy-qwen2") model = PeftModel.from_pretrained(base_model, "vmpsergio/83ccc229-46a5-4ad6-a1b2-8f438794426e") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/training_args.bin from vmpsergio/83ccc229-46a5-4ad6-a1b2-8f438794426e: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/vmpsergio/83ccc229-46a5-4ad6-a1b2-8f438794426e/resolve/main/last-checkpoint/training_args.bin
- Command line
-
hf download hf://vmpsergio/83ccc229-46a5-4ad6-a1b2-8f438794426e/last-checkpoint/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/vmpsergio/83ccc229-46a5-4ad6-a1b2-8f438794426e/resolve/main/last-checkpoint/training_args.bin
6.78 kB
- Xet hash:
- 8d2da68e62db7dcfe3bbd2496cd628962c52dadb2def40d5ecdff2fa722630e0
- Size of remote file:
- 6.78 kB
- SHA256:
- 51fb6fbee5d742332d4dd11c9234fa6fd3dc95c4ad68f94f7002cc7ffd990a91
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