Text Classification
Transformers
Safetensors
English
firstname_gender
feature-extraction
gender-classification
first-name
tiny-models
spiceechat
causal-lm
custom_code
Instructions to use SpiceeChat/FirstName-Genre-Classifier-30M-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SpiceeChat/FirstName-Genre-Classifier-30M-SFT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SpiceeChat/FirstName-Genre-Classifier-30M-SFT", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SpiceeChat/FirstName-Genre-Classifier-30M-SFT", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "F_ID": 42, | |
| "M_ID": 49, | |
| "architectures": [ | |
| "FirstNameGenderForCausalLM" | |
| ], | |
| "attention_backend": "pytorch", | |
| "auto_map": { | |
| "AutoConfig": "modeling_firstname_gender.FirstNameGenderConfig", | |
| "AutoModel": "modeling_firstname_gender.FirstNameGenderForCausalLM", | |
| "AutoModelForCausalLM": "modeling_firstname_gender.FirstNameGenderForCausalLM" | |
| }, | |
| "bos_token_id": 2, | |
| "ctx_len": 20, | |
| "dropout": 0.0, | |
| "dtype": "float32", | |
| "eos_token_id": 3, | |
| "hidden_size": 384, | |
| "max_position_embeddings": 20, | |
| "model_type": "firstname_gender", | |
| "n_embd": 384, | |
| "n_head": 4, | |
| "n_layer": 4, | |
| "num_attention_heads": 4, | |
| "num_hidden_layers": 4, | |
| "pad_token_id": 0, | |
| "transformers_version": "5.10.2", | |
| "use_cache": false, | |
| "vocab_size": 32768 | |
| } | |