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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:
# 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
| { | |
| "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" | |
| } | |
| } | |