Feature Extraction
sentence-transformers
Safetensors
Transformers
qwen2_5_omni_thinker
image-text-to-text
multimodal-embedding
Instructions to use LCO-Embedding/LCO-Embedding-Omni-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LCO-Embedding/LCO-Embedding-Omni-7B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LCO-Embedding/LCO-Embedding-Omni-7B") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use LCO-Embedding/LCO-Embedding-Omni-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="LCO-Embedding/LCO-Embedding-Omni-7B")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("LCO-Embedding/LCO-Embedding-Omni-7B") model = AutoModelForMultimodalLM.from_pretrained("LCO-Embedding/LCO-Embedding-Omni-7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 3,435 Bytes
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"_attn_implementation_autoset": true,
"architectures": [
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],
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"encoder_layerdrop": 0.0,
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"init_std": 0.02,
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"max_source_positions": 1500,
"model_type": "qwen2_5_omni_audio_encoder",
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"ignore_index": -100,
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"model_type": "qwen2_5_omni_thinker",
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"text_config": {
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"model_type": "qwen2_5_omni_text",
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"num_key_value_heads": 4,
"rms_norm_eps": 1e-06,
"rope_scaling": {
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],
"rope_type": "default",
"type": "default"
},
"rope_theta": 1000000.0,
"sliding_window": null,
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"vocab_size": 152064
},
"transformers_version": "4.57.0.dev0",
"user_token_id": 872,
"video_token_index": 151656,
"vision_config": {
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"depth": 32,
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"temporal_patch_size": 2,
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}
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