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
File size: 601 Bytes
6611f0e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | {
"arm": "split",
"attn_impl": "flex",
"qk_norm": true,
"output_dir": "/scratch/11079/antonxue/tridlm-runs/20260829-1047-qk1-lr1e-4-split",
"learning_rate": 0.0001,
"warmup_steps": 1000,
"stable_steps": 89000,
"decay_steps": 10000,
"min_lr_ratio": 0.0,
"weight_decay": 0.1,
"batch_size": 64,
"gradient_accumulation_steps": 1,
"logging_steps": 5,
"save_steps": 1000,
"save_total_limit": 5,
"keep_every": 5000,
"seed": 1,
"dataloader_num_workers": 4,
"compile": true,
"resume_from_checkpoint": null,
"tiny": false,
"synthetic_rows": 0,
"max_train_rows": 0
} |