Feature Extraction
Transformers
Safetensors
English
qwen2_vl
image-text-to-text
multimodal-embedding
universal-multimodal-embedding
retrieval
latent-reasoning
mllm
qwen2-vl
Instructions to use Rem520/PLUME-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rem520/PLUME-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Rem520/PLUME-7B")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("Rem520/PLUME-7B") model = AutoModelForImageTextToText.from_pretrained("Rem520/PLUME-7B") - Notebooks
- Google Colab
- Kaggle
| { | |
| "attn_implementation": "flash_attention_2", | |
| "do_sample": true, | |
| "eos_token_id": [ | |
| 151645, | |
| 151643 | |
| ], | |
| "pad_token_id": 151643, | |
| "transformers_version": "4.57.0" | |
| } | |