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
File size: 613 Bytes
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"additional_special_tokens": [
"<|im_start|>",
"<|im_end|>",
"<|object_ref_start|>",
"<|object_ref_end|>",
"<|box_start|>",
"<|box_end|>",
"<|quad_start|>",
"<|quad_end|>",
"<|vision_start|>",
"<|vision_end|>",
"<|vision_pad|>",
"<|image_pad|>",
"<|video_pad|>"
],
"eos_token": {
"content": "<|im_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"pad_token": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
}
|