Video-Text-to-Text
Transformers
PyTorch
Safetensors
English
Chinese
llama
text-generation
custom_code
text-generation-inference
Instructions to use KangarooGroup/kangaroo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KangarooGroup/kangaroo with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("KangarooGroup/kangaroo", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("KangarooGroup/kangaroo", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Jie Hu commited on
Update README.md
Browse files
README.md
CHANGED
|
@@ -7,7 +7,7 @@ pipeline_tag: visual-question-answering
|
|
| 7 |
---
|
| 8 |
# Kangaroo: A Powerful Video-Language Model Supporting Long-context Video Input
|
| 9 |
|
| 10 |
-
**Kangaroo** has been released. Please check out our [blog](https://kangaroogroup.github.io/Kangaroo.github.io/) and [github](https://github.com/KangarooGroup/Kangaroo) for details.
|
| 11 |
|
| 12 |
## Abstract
|
| 13 |
We introduce <strong>Kangaroo</strong>, a powerful Multimodal Large Language Model designed for long-context video understanding. Our presented Kangaroo model shows remarkable performance across diverse video understanding tasks including video caption, QA and conversation. Generally, our key contributions in this work can be summarized as follows:
|
|
|
|
| 7 |
---
|
| 8 |
# Kangaroo: A Powerful Video-Language Model Supporting Long-context Video Input
|
| 9 |
|
| 10 |
+
**Kangaroo** has been released. Please check out our [paper](https://arxiv.org/pdf/2408.15542), [blog](https://kangaroogroup.github.io/Kangaroo.github.io/) and [github](https://github.com/KangarooGroup/Kangaroo) for details.
|
| 11 |
|
| 12 |
## Abstract
|
| 13 |
We introduce <strong>Kangaroo</strong>, a powerful Multimodal Large Language Model designed for long-context video understanding. Our presented Kangaroo model shows remarkable performance across diverse video understanding tasks including video caption, QA and conversation. Generally, our key contributions in this work can be summarized as follows:
|