Instructions to use leafyseay/RIME-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leafyseay/RIME-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="leafyseay/RIME-7B")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("leafyseay/RIME-7B") model = AutoModelForMultimodalLM.from_pretrained("leafyseay/RIME-7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add pipeline tag and library name metadata
#1
by nielsr HF Staff - opened
README.md
CHANGED
|
@@ -1,10 +1,12 @@
|
|
| 1 |
---
|
| 2 |
license: apache-2.0
|
|
|
|
|
|
|
| 3 |
---
|
| 4 |
|
| 5 |
# RIME-7B
|
| 6 |
|
| 7 |
-
**RIME** (**R**ewrite-dr**I**ven **M**ultimodal **E**mbedding) model based on Qwen2-VL-7B-Instruct.
|
| 8 |
|
| 9 |
## Model Description
|
| 10 |
|
|
@@ -23,4 +25,4 @@ See the [RIME repository](https://github.com/PeppaWu/RIME) for inference and eva
|
|
| 23 |
journal={arXiv preprint arXiv:2604.22280},
|
| 24 |
year={2026}
|
| 25 |
}
|
| 26 |
-
```
|
|
|
|
| 1 |
---
|
| 2 |
license: apache-2.0
|
| 3 |
+
library_name: transformers
|
| 4 |
+
pipeline_tag: feature-extraction
|
| 5 |
---
|
| 6 |
|
| 7 |
# RIME-7B
|
| 8 |
|
| 9 |
+
**RIME** (**R**ewrite-dr**I**ven **M**ultimodal **E**mbedding) is a generative multimodal embedding model based on Qwen2-VL-7B-Instruct, presented in the paper [Beyond Chain-of-Thought: Rewrite as a Universal Interface for Generative Multimodal Embeddings](https://huggingface.co/papers/2604.22280).
|
| 10 |
|
| 11 |
## Model Description
|
| 12 |
|
|
|
|
| 25 |
journal={arXiv preprint arXiv:2604.22280},
|
| 26 |
year={2026}
|
| 27 |
}
|
| 28 |
+
```
|