Instructions to use DataCanvas/MMAlaya with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DataCanvas/MMAlaya with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="DataCanvas/MMAlaya", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("DataCanvas/MMAlaya", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -13,8 +13,6 @@ MMAlaya包含以下三个模块:
|
|
| 13 |
模型的训练主要基于[LLaVA](https://github.com/haotian-liu/LLaVA)架构
|
| 14 |
|
| 15 |
OpenCompass 评测榜单,均分41.1,排名25名。
|
| 16 |
-

|
| 17 |
MMBench 评测榜单,开源开放的模型,中文测试集,均分58.6,排名25名。
|
| 18 |
-

|
| 19 |
|
| 20 |
推理可以参考 [inference.py](https://github.com/DataCanvasIO/MMAlaya/blob/main/inference.py)
|
|
|
|
| 13 |
模型的训练主要基于[LLaVA](https://github.com/haotian-liu/LLaVA)架构
|
| 14 |
|
| 15 |
OpenCompass 评测榜单,均分41.1,排名25名。
|
|
|
|
| 16 |
MMBench 评测榜单,开源开放的模型,中文测试集,均分58.6,排名25名。
|
|
|
|
| 17 |
|
| 18 |
推理可以参考 [inference.py](https://github.com/DataCanvasIO/MMAlaya/blob/main/inference.py)
|