Feature Extraction
Transformers
Safetensors
chest2vec_embedding
text-embeddings
retrieval
radiology
chest
qwen
custom_code
Instructions to use lukeingawesome/chest2vec_4b_chest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lukeingawesome/chest2vec_4b_chest with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="lukeingawesome/chest2vec_4b_chest", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lukeingawesome/chest2vec_4b_chest", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from lukeingawesome/chest2vec_4b_chest: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/lukeingawesome/chest2vec_4b_chest/resolve/main/tokenizer.json
- Command line
-
hf download hf://lukeingawesome/chest2vec_4b_chest/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/lukeingawesome/chest2vec_4b_chest/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 7f26d71aeab40e6bac513acbfab71f48109e92ee379f5fc5fe23dba262364317
- Size of remote file:
- 11.4 MB
- SHA256:
- 83cdf8c3a34f68862319cb1810ee7b1e2c0a44e0864ae930194ddb76bb7feb8d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.