Visual Question Answering
Transformers
Safetensors
baby_medgemma
feature-extraction
medical
chest-x-ray
vision-language-model
interpretability
mechanistic-interpretability
research
custom_code
Instructions to use saillab/FlipLens with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use saillab/FlipLens with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="saillab/FlipLens", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("saillab/FlipLens", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "BabyMedGemmaForVQA" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "modeling_babymedgemma.BabyMedGemmaConfig", | |
| "AutoModel": "modeling_babymedgemma.BabyMedGemmaForVQA" | |
| }, | |
| "dtype": "float32", | |
| "ground_dim": 1152, | |
| "hidden_size": 384, | |
| "id2label": { | |
| "0": "no", | |
| "1": "yes" | |
| }, | |
| "label2id": { | |
| "no": 0, | |
| "yes": 1 | |
| }, | |
| "max_len": 20, | |
| "model_type": "baby_medgemma", | |
| "n_img": 256, | |
| "no_id": 50, | |
| "num_hidden_layers": 6, | |
| "tokenizer_hf_ids": [ | |
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| ], | |
| "tokenizer_name": "google/medgemma-4b-it", | |
| "transformers_version": "4.57.3", | |
| "use_ground": true, | |
| "vision_dim": 1152, | |
| "vocab_size": 141, | |
| "yes_id": 64 | |
| } |