Feature Extraction
Transformers
Safetensors
chest2vec
text-embeddings
retrieval
radiology
chest
qwen
custom_code
Instructions to use chest2vec/chest2vec_4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chest2vec/chest2vec_4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="chest2vec/chest2vec_4B", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("chest2vec/chest2vec_4B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 355 Bytes
c036088 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | {
"model_type": "chest2vec",
"architectures": [
"Chest2VecModel"
],
"auto_map": {
"AutoConfig": "configuration_chest2vec.Chest2VecConfig",
"AutoModel": "modeling_chest2vec.Chest2VecModel"
},
"base_model": "Qwen/Qwen3-Embedding-4B",
"adapter_subdir": "contrastive",
"require_flash_attention_2": true,
"default_max_len": 512
}
|