Instructions to use Databook/SmolClassifierMed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Databook/SmolClassifierMed with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Databook/SmolClassifierMed")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Databook/SmolClassifierMed") model = AutoModel.from_pretrained("Databook/SmolClassifierMed", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from Databook/SmolClassifierMed: direct link, hf CLI and curl.
- Browser
- Download file 714 Bytes
-
https://huggingface.co/Databook/SmolClassifierMed/resolve/main/config.json
- Command line
-
hf download hf://Databook/SmolClassifierMed/config.json
-
curl -L -o config.json https://huggingface.co/Databook/SmolClassifierMed/resolve/main/config.json
714 Bytes
| { | |
| "_name_or_path": "HuggingFaceTB/SmolLM-360M", | |
| "architectures": [ | |
| "LlamaModel" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 0, | |
| "eos_token_id": 0, | |
| "head_dim": 64, | |
| "hidden_act": "silu", | |
| "hidden_size": 960, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 2560, | |
| "max_position_embeddings": 2048, | |
| "mlp_bias": false, | |
| "model_type": "llama", | |
| "num_attention_heads": 15, | |
| "num_hidden_layers": 32, | |
| "num_key_value_heads": 5, | |
| "pretraining_tp": 1, | |
| "rms_norm_eps": 1e-05, | |
| "rope_scaling": null, | |
| "rope_theta": 10000.0, | |
| "tie_word_embeddings": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.46.2", | |
| "use_cache": true, | |
| "vocab_size": 49152 | |
| } | |