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
| torch==2.6.0 | |
| torchvision==0.21.0 | |
| torchaudio==2.6.0 | |
| transformers==4.57.3 | |
| trl==0.9.3 | |
| deepspeed==0.16.9 | |
| https://github.com/Dao-AILab/flash-attention/releases/download/v2.8.3/flash_attn-2.8.3+cu12torch2.6cxx11abiTRUE-cp310-cp310-linux_x86_64.whl | |
| peft | |
| huggingface_hub | |
| bitsandbytes | |
| accelerate | |
| numpy |