Instructions to use TSunm/InternVL2-1B-ViVQA-X with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TSunm/InternVL2-1B-ViVQA-X with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="TSunm/InternVL2-1B-ViVQA-X", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TSunm/InternVL2-1B-ViVQA-X", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files
README.md
CHANGED
|
@@ -25,7 +25,7 @@ print(output["generated_text"])
|
|
| 25 |
|
| 26 |
## Training procedure
|
| 27 |
|
| 28 |
-
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/minhdeptrai/huggingface/runs/
|
| 29 |
|
| 30 |
|
| 31 |
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
|
|
|
|
| 25 |
|
| 26 |
## Training procedure
|
| 27 |
|
| 28 |
+
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/minhdeptrai/huggingface/runs/s4czcbh0)
|
| 29 |
|
| 30 |
|
| 31 |
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
|