Fill-Mask
Transformers
Safetensors
nucengram
feature-extraction
biology
genomics
dna
masked-lm
custom_code
Instructions to use FreakingPotato/NucEngram with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FreakingPotato/NucEngram with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="FreakingPotato/NucEngram", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("FreakingPotato/NucEngram", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_type": "nucengram", | |
| "architectures": [ | |
| "NucEngramModel" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_nucengram.NucEngramConfig", | |
| "AutoModel": "modeling_nucengram.NucEngramModel", | |
| "AutoModelForMaskedLM": "modeling_nucengram.NucEngramForMaskedLM" | |
| }, | |
| "backbone": { | |
| "vocab_size": 9, | |
| "hidden_size": 384, | |
| "intermediate_size": 1024, | |
| "num_hidden_layers": 8, | |
| "num_attention_heads": 6, | |
| "max_position_embeddings": 8192, | |
| "local_attention": 128, | |
| "rope_theta_global": 160000.0, | |
| "rope_theta_local": 10000.0, | |
| "attn_implementation": "sdpa", | |
| "tie_word_embeddings": true | |
| }, | |
| "engram": { | |
| "ngram_orders": [ | |
| 3, | |
| 4, | |
| 5, | |
| 6, | |
| 8 | |
| ], | |
| "n_heads_per_order": 4, | |
| "d_mem": 48, | |
| "layer_inject_ids": [ | |
| 1, | |
| 4 | |
| ], | |
| "use_conv": true, | |
| "gate_temp": 1.0, | |
| "seed": 0, | |
| "hidden_size": 384, | |
| "vocab_size": 9, | |
| "pad_id": 0 | |
| }, | |
| "max_length": 8192, | |
| "pad_token_id": 0, | |
| "hidden_size": 384, | |
| "torch_dtype": "float32", | |
| "transformers_version": "5.7.0" | |
| } |