Text Classification
Transformers
Safetensors
English
mimelens
image-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
File size: 790 Bytes
d6f1260 85ca285 d6f1260 85ca285 d6f1260 | 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": [
"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
} |