Feature Extraction
Transformers
Safetensors
English
visual-document-retrieval
multi-vector
late-interaction
colbert
index-compression
memory-tokens
text-to-image
Instructions to use hltcoe/MemTok_qwen2.5-vl_colpali with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hltcoe/MemTok_qwen2.5-vl_colpali with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hltcoe/MemTok_qwen2.5-vl_colpali")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hltcoe/MemTok_qwen2.5-vl_colpali", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -76,7 +76,7 @@ from src.encoder.multivec_encoder import MultiVecEncoder
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from src.models.qwen2_5_vl_embed.qwen2_5_vl_embed import Qwen2_5ForEmbedding
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from src.utils import get_appending_token_strings
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MODEL_ID = "
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IMAGE_PATH = "PLACEHOLDER"
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NUM_MEMORY_TOKENS = 64
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APPENDING_SUFFIX = "".join(get_appending_token_strings(NUM_MEMORY_TOKENS))
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from src.models.qwen2_5_vl_embed.qwen2_5_vl_embed import Qwen2_5ForEmbedding
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from src.utils import get_appending_token_strings
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MODEL_ID = "hltcoe/MemTok_qwen2.5-vl_colpali"
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IMAGE_PATH = "PLACEHOLDER"
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NUM_MEMORY_TOKENS = 64
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APPENDING_SUFFIX = "".join(get_appending_token_strings(NUM_MEMORY_TOKENS))
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