This repository provides pre-converted GGUF files for the BAAI BGE-M3 embedding model, enhanced to output all three embedding types from a single inference call:

Head	Output	Shape	Use Case
Dense	CLS pooling → L2 normalization	`float[1024]`	Semantic similarity (cosine)
Sparse	`linear → ReLU → log(1+x)` per token	`[token_id, weight]` pairs	Lexical / hybrid search
ColBERT	`linear → L2 norm` per token	`n_tokens × float[1024]`	Token-level late interaction

A single llama_decode() computes all three. No separate models, no external BM25.

Unlike the standard BGE-M3 GGUF (which only preserves the dense head), these files contain the full cls.sparse and cls.colbert weights extracted from the original sentence-transformers checkpoint.

Available Files

File	Quantization	Size	Heads
`bge-m3-multivector-q8.gguf`	Q8_0	~600 MB	Dense + Sparse + ColBERT
`bge-m3-multivector-f16.gguf`	F16	~1.1 GB	Dense + Sparse + ColBERT

Q8_0 is recommended for production — identical quality, half the size.

Usage

With llama.cpp-mv (recommended)

Use the companion llama.cpp-mv fork, which supports all three heads natively. Start the server:

bash

llama-server --model bge-m3-multivector-q8.gguf \
    --pooling cls --no-mmap --embedding \
    --host 0.0.0.0 --port 8080 \
    -c 8192 -b 8192 -ub 8192 -np 1 --fit off \
    -ngl 99

Companion fork with inference code: [llama.cpp-mv](https://github.com/iz0eyj/llama.cpp-mv)

Default call returns dense + sparse:

bash

curl -X POST http://127.0.0.1:8080/v1/embeddings \
  -H "Content-Type: application/json" \
  -d '{"input": "Samarcanda è una città meravigliosa"}'

Add ?colbert=true (or "colbert": true in the JSON body) for all three:

bash

curl -X POST "http://127.0.0.1:8080/v1/embeddings?colbert=true" \
  -H "Content-Type: application/json" \
  -d '{"input": "Samarcanda è una città meravigliosa"}'

JSON Response Format

json

{
  "data": [{
    "embedding": [0.001, -0.023, ...],           // dense: 1024 floats
    "sparse_embedding": [                          // sparse: [token_id, weight] pairs
      [0,       0.116],
      [121283,  0.154],
      [2,       0.095]
    ],
    "colbert_embedding": [                         // colbert: n_tokens × 1024
      [-0.768, -0.048, ...],
      [ 0.312,  1.205, ...],
      [-0.015, -0.823, ...]
    ],
    "index": 0,
    "object": "embedding"
  }],
  "model": "bge-m3-multivector-q8.gguf",
  "usage": {"prompt_tokens": 5, "total_tokens": 5}
}

colbert_embedding is only present when ?colbert=true is set
Sparse weights > 0 indicate lexically active tokens
ColBERT vectors are L2-normalized per token (norm ≈ 32, use dot product for similarity)

Per-Token ColBERT Similarity (Late Interaction)

python

import numpy as np

def colbert_score(vecs_a, vecs_b):
    """MaxSim per token, then average."""
    sim = np.dot(vecs_a, vecs_b.T)  # [n_a, n_b]
    return np.mean(np.max(sim, axis=1))

# Example: "Samarcanda è bella" vs "Samarcanda è meravigliosa"
# score ≈ 1010 (similar) vs ≈ 330 (different topic)

How These GGUF Files Were Created

The standard llama.cpp converter strips tensors it doesn't recognize. BGE-M3's sparse and colbert heads (sparse_linear.pt, colbert_linear.pt) are stored separately from the main model checkpoint and were silently skipped.

Our converter patches (in llama.cpp-mv):

gguf-py/gguf/constants.py — Added CLS_SPARSE and CLS_COLBERT tensor types
gguf-py/gguf/tensor_mapping.py — Mapped sparse_linear → cls.sparse, colbert_linear → cls.colbert
conversion/bert.py — Load .pt files from model directory via generate_extra_tensors()

Command:

python convert_hf_to_gguf.py BAAI/bge-m3 --outtype q8_0 --outfile bge-m3-multivector-q8.gguf

Inference Architecture (llama.cpp-mv)

The companion C++ fork adds three separate output paths from the same hidden states:

Transformer (24 layers XLM-RoBERTa)
    │
    ▼ hidden_states [1024, n_tokens]
    │
    ├── [CLS] ──► L2 normalize ──► dense [1024]
    ├── sparse_linear → ReLU → log1p ──► sparse [n_tokens] scalars
    └── colbert_linear → L2 norm ──► colbert [n_tokens × 1024]

Key design decisions:

log1p for sparse is applied on CPU after GPU transfer (avoids new GGML op)
ColBERT uses ggml_norm for per-token L2 normalization
Output buffers are sized for max batch size, zero overhead for small requests

Limitations

ColBERT scales with token count — 1000 tokens = 4 MB per document. Best used on short text (search queries, titles, paragraphs). For long documents, pre-filter with dense search, then apply ColBERT to top-K candidates.
CPU mode is unstable on some upstream llama.cpp versions due to F16/F32 mixed-type operations. Use Vulkan (`-ngl 99

Companion fork with inference code: llama.cpp-mv `) or CUDA.

The ColBERT head is 1024×1024 (4 MB) — negligible compared to the 600 MB model body.

License

MIT — matches both BAAI/bge-m3 and llama.cpp.

Credits

BAAI for BGE-M3 model
llama.cpp by ggerganov
GGUF conversion and inference patches by iz0eyj
Companion fork: llama.cpp-mv

Uncino/bge-m3-multivector-gguf

BGE-M3 GGUF — Dense + Sparse + ColBERT

Available Files

Usage

With llama.cpp-mv (recommended)

JSON Response Format

Per-Token ColBERT Similarity (Late Interaction)

How These GGUF Files Were Created

Inference Architecture (llama.cpp-mv)

Limitations

License

Credits

Uncino/
bge-m3-multivector-gguf