Instructions to use HuyTran1301/CodeT5p-ModelPhase2-05072025-8ep with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuyTran1301/CodeT5p-ModelPhase2-05072025-8ep with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("HuyTran1301/CodeT5p-ModelPhase2-05072025-8ep") model = AutoModelForSeq2SeqLM.from_pretrained("HuyTran1301/CodeT5p-ModelPhase2-05072025-8ep", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from HuyTran1301/CodeT5p-ModelPhase2-05072025-8ep: direct link, hf CLI and curl.
- Browser
- Download file 5.5 kB
-
https://huggingface.co/HuyTran1301/CodeT5p-ModelPhase2-05072025-8ep/resolve/main/training_args.bin
- Command line
-
hf download hf://HuyTran1301/CodeT5p-ModelPhase2-05072025-8ep/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/HuyTran1301/CodeT5p-ModelPhase2-05072025-8ep/resolve/main/training_args.bin
5.5 kB
- Xet hash:
- ce4496174abbae449109e20925a5627b1a997449b1237b6e810033e044026c8e
- Size of remote file:
- 5.5 kB
- SHA256:
- 4700f6f27e531d74a440292a3494ff83bdbc47e26b514817f11952723b950eeb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.