WeMM-Embedding-2B GGUF (unofficial)
Unofficial GGUF conversion of tencent/WeMM-Embedding-2B for llama.cpp. It is the default embedding model of imgsearch, a local-first image search app. Not affiliated with or endorsed by Tencent. All credit for the model goes to the WeChat Vision Team; it is licensed under Apache 2.0.
| File |
Size |
SHA-256 |
WeMM-Embedding-2B-Q4_K_M.gguf |
1.56 GB |
3c6aa2dab16f250f730e7e87305fbd637a8fd068770d3cae16a0c16e33678d6d |
mmproj-WeMM-Embedding-2B-f16.gguf |
0.67 GB |
02115c4aebe057aaf0473c8c9ee4efbbd080ae1b07386910cb326c1c042baaaa |
Usage
Both files are needed for image input. Output: 2048 dimensions, L2-normalize; Matryoshka truncation (re-normalize) to 1024/512/256 works well.
The prompt must match the reference processor output exactly. No system turn, no instruction; queries and documents are encoded the same way. Take the last-token hidden state (pooling none, last position, or --pooling last):
text: <|im_start|>user\n{text}<|im_end|>\n<embedding>
image: <|im_start|>user\n<__media__><|im_end|>\n<embedding>
<__media__> is the llama.cpp mtmd media marker; mtmd adds the vision start/end tokens. <embedding> (id 248077) is a control token and must be parsed as special.
Conversion
Made with llama.cpp 5343f45 (convert_hf_to_gguf.py, llama-quantize) from the official safetensors, with two adjustments:
- WeMM's
tokenizer.json adds a normalizer (strips a trailing \n) and a post-processor (appends <embedding>). Both change the converter's pre-tokenizer hash. The conversion used a copy with normalizer set to {"type": "NFC"} and post_processor set to null. Vocabulary and merges are unchanged; the prompt above adds <embedding> explicitly.
--no-mtp: the config declares an MTP layer that the weights do not contain.
Text model: f16 → Q4_K_M. Vision projector: f16.
Evaluation
Flickr30k 1k test (1,000 images, 5,000 captions), images resized to a maximum side of 384 px, through imgsearch's llama.cpp pipeline:
| Model |
Quant |
T2I R@1 |
T2I R@5 |
I2T R@1 |
ms/image (M2 Max) |
ms/image (Radeon 780M) |
| Qwen3-VL-Embedding-2B |
Q6_K |
80.8 |
94.7 |
94.3 |
242 |
– |
| Qwen3-VL-Embedding-8B |
Q4_K_M |
81.1 |
95.0 |
94.5 |
799 |
1095 |
| WeMM-Embedding-2B |
Q6_K |
84.2 |
96.6 |
94.7 |
202 |
– |
| WeMM-Embedding-2B (this repo) |
Q4_K_M |
84.1 |
96.5 |
95.0 |
236 |
421 |
Against the transformers reference, text embeddings reach cosine ≈0.99 at Q4_K_M (0.9999 at Q8_0). Image embeddings reach 0.95–0.97 with identical pixels; this gap comes from llama.cpp's vision implementation, not from quantization.
