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pg_colbert_v1 layout containing backbone transformer weights, tokenizer metadata, ColBERT dense projection layers, and similarity metrics.pg_colbert_v1 schema)F16pg_colbert_profile_v1bothPASSEDYESPASSED1// Initialize the ColBERT GGML model context
2colbert_model model = colbert_model_load("SauerkrautLM-Multi-ModernColBERT.f16.gguf");
3
4// Tokenize and encode queries into late-interaction token embeddings
5std::vector<float> query_embeddings = colbert_encode_query(model, "Which planet is known as the Red Planet?");
6
7// Tokenize and encode documents into late-interaction token embeddings
8std::vector<float> doc_embeddings = colbert_encode_doc(model, "Mars is the Red Planet.");convert_colbert_hf_to_gguf.py utility.python tools/convert_colbert_hf_to_gguf.py --model-id VAGOsolutions/SauerkrautLM-Multi-ModernColBERT --outfile VAGOsolutions_SauerkrautLM_Multi_ModernColBERT.f16.gguf --outtype f16 --target-runtime both1.0.0