Other
Transformers
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English
diffusion_lm
fill-mask
custom_code
tiny-llm-ablation
from-scratch
diffusion
masked-language-modeling
Eval Results (legacy)
Instructions to use d0rj/diffusion-51M-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use d0rj/diffusion-51M-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("d0rj/diffusion-51M-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 839 Bytes
80aea5b | 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 26 27 28 29 30 31 32 | {
"architectures": [
"DiffusionLMForMaskedLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "float32",
"eos_token_id": 2,
"head_dim": 64,
"hidden_size": 512,
"intermediate_size": 1536,
"mask_token_id": 32768,
"max_position_embeddings": 2048,
"model_type": "diffusion_lm",
"num_attention_heads": 8,
"num_diffusion_steps": 64,
"num_hidden_layers": 10,
"pad_token_id": 0,
"rms_norm_eps": 1e-05,
"rope_theta": 10000.0,
"tie_word_embeddings": true,
"time_conditioning": "none",
"time_conditioning_scale": 0.02,
"training_objective": "absorbing-linear-1overT-v2",
"transformers_version": "5.17.0",
"vocab_size": 32768,
"auto_map": {
"AutoConfig": "configuration_diffusion_lm.DiffusionLMConfig",
"AutoModelForMaskedLM": "modeling_diffusion_lm.DiffusionLMForMaskedLM"
}
}
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