Feature Extraction
Transformers
Safetensors
English
tridlm
masked-diffusion
diffusion-language-model
gpt2
openwebtext
triangular-attention
custom_code
Instructions to use EER6/TriDLM-124M-split with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EER6/TriDLM-124M-split with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="EER6/TriDLM-124M-split", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("EER6/TriDLM-124M-split", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from EER6/TriDLM-124M-split: direct link, hf CLI and curl.
- Browser
- Download file 3.56 MB
-
https://huggingface.co/EER6/TriDLM-124M-split/resolve/main/tokenizer.json
- Command line
-
hf download hf://EER6/TriDLM-124M-split/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/EER6/TriDLM-124M-split/resolve/main/tokenizer.json
3.56 MB
File too large to display, you can check the raw version instead.