Instructions to use Raghavan/beit3_base_patch16_480_vqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Raghavan/beit3_base_patch16_480_vqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Raghavan/beit3_base_patch16_480_vqa")# Load model directly from transformers import AutoModelForQuestionAnswering model = AutoModelForQuestionAnswering.from_pretrained("Raghavan/beit3_base_patch16_480_vqa", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload 6 files
Browse files- config.json +0 -0
- sentencepiece.bpe.model +2 -2
config.json
CHANGED
|
The diff for this file is too large to render.
See raw diff
|
|
|
sentencepiece.bpe.model
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:6f5e2fefcf793761a76a6bfb8ad35489f9c203b25557673284b6d032f41043f4
|
| 3 |
+
size 1356293
|