Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use CCChenRyan/LLM_T1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CCChenRyan/LLM_T1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CCChenRyan/LLM_T1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CCChenRyan/LLM_T1") model = AutoModelForSequenceClassification.from_pretrained("CCChenRyan/LLM_T1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download run-1/checkpoint-1069/optimizer.pt from CCChenRyan/LLM_T1: direct link, hf CLI and curl.
- Browser
- Download file 536 MB
-
https://huggingface.co/CCChenRyan/LLM_T1/resolve/main/run-1/checkpoint-1069/optimizer.pt
- Command line
-
hf download hf://CCChenRyan/LLM_T1/run-1/checkpoint-1069/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/CCChenRyan/LLM_T1/resolve/main/run-1/checkpoint-1069/optimizer.pt
536 MB
- Xet hash:
- acb14223396e4136a81c82dd0a02e323f2018d2b1a699713ae839144c0ed0c6c
- Size of remote file:
- 536 MB
- SHA256:
- 758a74fe0d4e1ce5944f7f19a989323a3cb08aa76049601dcfa3c54485cab57d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.