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johannhartmann/SauerkrautLM-Multi-ColBERT-33m-GGUF
SauerkrautLM-Multi-ColBERT-33m-GGUF is a machine learning model from johannhartmann. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for sentence-transformers. The card lists the license as apache-2.0.
This repository contains GGUF format weights for the ColBERT retrieval model VAGOsolutions/SauerkrautLM-Multi-ColBERT-33m, converted specifically for our GGML-based ColBERT C++ runtime.
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From the Hugging Face model README
This repository contains GGUF format weights for the ColBERT retrieval model VAGOsolutions/SauerkrautLM-Multi-ColBERT-33m, converted specifically for our GGML-based ColBERT C++ runtime.
This GGUF file is NOT a standard llama.cpp decoder model. It uses the pg_colbert_v1 layout containing backbone transformer weights, tokenizer metadata, ColBERT dense projection layers, and similarity metrics.
pg_colbert_v1 schema)F16pg_colbert_profile_v1bothPASSEDYESPASSEDThis GGUF model is designed to be loaded directly by our custom C++ ColBERT runtime (which uses GGML for the ModernBERT/BERT backbone forward pass and executes the ColBERT late-interaction dense projection).
// Initialize the ColBERT GGML model context
colbert_model model = colbert_model_load("SauerkrautLM-Multi-ColBERT-33m.f16.gguf");
// Tokenize and encode queries into late-interaction token embeddings
std::vector<float> query_embeddings = colbert_encode_query(model, "Which planet is known as the Red Planet?");
// Tokenize and encode documents into late-interaction token embeddings
std::vector<float> doc_embeddings = colbert_encode_doc(model, "Mars is the Red Planet.");
Generated using the convert_colbert_hf_to_gguf.py utility.
python tools/convert_colbert_hf_to_gguf.py --model-id VAGOsolutions/SauerkrautLM-Multi-ColBERT-33m --outfile VAGOsolutions_SauerkrautLM_Multi_ColBERT_33m.f16.gguf --outtype f16 --target-runtime both1.0.0