Feature Extraction
Transformers
Safetensors
chest2vec
text-embeddings
retrieval
radiology
chest
qwen
custom_code
Instructions to use chest2vec/chest2vec_0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chest2vec/chest2vec_0.6B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="chest2vec/chest2vec_0.6B", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("chest2vec/chest2vec_0.6B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_type": "chest2vec", | |
| "architectures": [ | |
| "Chest2VecModel" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_chest2vec.Chest2VecConfig", | |
| "AutoModel": "modeling_chest2vec.Chest2VecModel" | |
| }, | |
| "base_model": "Qwen/Qwen3-Embedding-0.6B", | |
| "adapter_subdir": "contrastive", | |
| "require_flash_attention_2": true, | |
| "default_max_len": 512 | |
| } | |