Text Classification
Transformers
Safetensors
English
mimelens
feature-extraction
file-type-detection
mime-classification
binary-content
binary-analysis
position-agnostic
libmagic
forensics
packet-inspection
byte-level
custom_code
Eval Results (legacy)
Instructions to use mjbommar/mimelens-001-tiny-byte-s2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mjbommar/mimelens-001-tiny-byte-s2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mjbommar/mimelens-001-tiny-byte-s2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mjbommar/mimelens-001-tiny-byte-s2", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "MimeLensModel" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_mimelens.MimeLensConfig", | |
| "AutoModel": "modeling_mimelens.MimeLensModel" | |
| }, | |
| "model_type": "mimelens", | |
| "torch_dtype": "float32", | |
| "vocab_size": 263, | |
| "hidden_size": 256, | |
| "num_hidden_layers": 4, | |
| "num_attention_heads": 4, | |
| "head_dim": 64, | |
| "ffn_multiplier_num": 8, | |
| "ffn_multiplier_den": 3, | |
| "max_position_embeddings": 1024, | |
| "rope_theta": 10000.0, | |
| "rms_norm_eps": 1e-06, | |
| "pad_token_id": 2, | |
| "cls_token_id": 4, | |
| "sep_token_id": 5, | |
| "mask_token_id": 6, | |
| "byte_offset": 7, | |
| "cls_pool_dim": 256, | |
| "mimelens_cell_id": "tiny/byte/s2", | |
| "mimelens_vocab_pipeline": "byte", | |
| "mimelens_tokenizer_hub_id": null, | |
| "mimelens_pretraining_steps": 22888, | |
| "mimelens_seed": 2 | |
| } |