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)# pip install -U transformers accelerate # 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_config.json from EER6/TriDLM-124M-split: direct link, hf CLI and curl.
- Browser
- Download file 340 Bytes
-
https://huggingface.co/EER6/TriDLM-124M-split/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://EER6/TriDLM-124M-split/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/EER6/TriDLM-124M-split/resolve/main/tokenizer_config.json
340 Bytes
| { | |
| "add_prefix_space": false, | |
| "backend": "tokenizers", | |
| "bos_token": "<|endoftext|>", | |
| "eos_token": "<|endoftext|>", | |
| "errors": "replace", | |
| "is_local": false, | |
| "local_files_only": true, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 1024, | |
| "pad_token": null, | |
| "tokenizer_class": "GPT2Tokenizer", | |
| "unk_token": "<|endoftext|>" | |
| } | |