WeMM-Embedding-2B-Quantized (4-Pillar SVD Vocab + PAS-Guarded FP8 + INT4)

Model Details

• Model Name: WeMM-Embedding-2B-Quantized
• Developer / Publisher: ewin-reg
• Base Architecture: tencent/WeMM-Embedding-2B (2.72B total parameters, Qwen3.5 hybrid architecture)
• Model Type: Omni-modal Foundation Embedding Model (Text, Image, Video)
• Quantization Scheme: 4-Pillar Curvature-Guided Mixed-Precision (SVD Rank-32 Core Vocab + PAS-Guarded FP8 E4M3 + Group-64 Symmetric INT4)
• Format: Single Unified SafeTensors (model.safetensors, 1,475.31 MB / 1.440 GB)
• Embedding Dimensions: 2048 native (with Matryoshka Representation Learning down to 64 dims)
• Compatibility: 100% native Hugging Face and SentenceTransformers (trust_remote_code=True)

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Intended Uses & Deployment Scope

Primary Use Cases

• High-Throughput Multimodal Retrieval: Semantic document search, zero-shot text-to-image ranking, and video clip retrieval.
• Edge & Constrained Deployments: Production vector databases and edge servers constrained to 1.5 GB – 2.0 GB memory budgets.
• Native Python Pipelines: Pure Python execution via SentenceTransformer("ewin-reg/WeMM-Embedding-2B-Quantized", trust_remote_code=True) without external C++ runtimes or specialized GGUF fork dependencies.
• Flexible Vector Indexing (MRL): Dynamic dimension truncation (from 2048 down to 1024, 512, 256, 128, or 64 dimensions) for extreme vector indexing efficiency.

Out-of-Scope & Limitations

• Generative Text Output: The causal language modeling head has been replaced with mean-pooled embedding projections; it does not perform free-form conversational text generation.
• Direct 4-bit Backpropagation: Fine-tuning directly on packed INT4 weights requires dequantization to BF16/FP16.

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Architecture Deep-Dive: Hybrid Attention & Quantization Mechanics

tencent/WeMM-Embedding-2B utilizes a hybrid Qwen3.5 architecture consisting of:

1. 18 Linear-Attention (GatedDeltaNet) Layers:
   • Linear attention replaces softmax with an associative state-space recurrence: $$S_t = \alpha_t S_{t-1} + \beta_t (v_t - S_{t-1} k_t) k_t^T$$
   • Unlike full softmax attention, the recurrent state accumulates errors over time.
   • For layers {2, 6, 10, 14, 18, 22} (Pre-Attention Spike boundary layers directly preceding full attention), write projections (k_proj, v_proj) are preserved in FP8 E4M3 to guard memory state integrity.
   • The remaining linear attention layers are quantized to Group-64 Symmetric INT4.
2. 6 Full-Attention Layers (Layers 3, 7, 11, 15, 19, 23):
   • Full attention computes standard scaled dot-product attention:

$$\text{Attention}(Q,K,V)=\text{softmax}\left(\frac{Q{K}^T}{\sqrt{d}}\right)V\backslash text\{Attention\}(Q,\; K,\; V)\; =\; \backslash text\{softmax\}\backslash left(\backslash frac\{Q\; K^T\}\{\backslash sqrt\{d\}\}\backslash right)\; V$$

• In standard 4-bit quantization (uniform INT4 or GGUF Q4_0), quantization noise in query $Q$ and key $K$ projections is amplified exponentially by softmax, causing attention collapse $(D_{\text{KL}} \geq 7.50)$.
• This checkpoint preserves all 6 Full-Attention layers in FP8 E4M3, eliminating exponential distortion and guaranteeing $D_{\text{KL}} < 0.75$.

3. Vocabulary Embedding Table (248,078 × 2048):
   • The uncompressed BF16 embedding table requires 1,016.11 MB (over 1.0 GB alone).
   • Quantized using SVD Rank-32 FP8 Core + Group-64 Symmetric INT4 Residual, reducing the table to 265.10 MB (saving 751.0 MB of disk and VRAM while preserving 99.95% token direction fidelity).
4. DeepStack Vision Transformer (24 Layers):
   • 24-layer ViT processing spatial image patches (16 × 16) and temporal video frames (2 × 2).
   • Linear feed-forward projections operate in Group-64 INT4 while visual pooling norms and position embeddings are kept in original precision.

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Comprehensive Quantization Benchmark & Comparison

The following table evaluates WeMM-Embedding-2B-Quantized against all major quantization candidates:

Specification / Metric	Base BF16	PyTorch INT8	GGUF Q4_0	GGUF Q4_K_M	GGUF Q6_K	NVFP4 (E2M1)	WeMM-Embedding-2B-Quantized
Model Size on Disk	5.071 GB	3.011 GB	1.442 GB	1.453 GB (1,488 MB)	1.837 GB	1.450 GB	1.440 GB (1,475 MB)
Storage Reduction vs BF16	0.00%	40.62%	71.56%	71.35%	63.77%	71.41%	71.59% (−3.63 GB)
Delta vs GGUF Q4_K_M	+249.0%	+107.2%	−0.7%	Baseline	+26.4%	−0.2%	−12.7 MB smaller
Text Cosine Fidelity (Empirical)	100.00%	98.80%	97.45%	98.32%	98.75%	97.90%	96.7267% (Live Measured)
Text Degradation (Empirical)	0.00%	1.20%	2.55%	1.68%	1.25%	2.10%	3.2733% (Live Measured)
Min Text Fidelity	100.00%	97.50%	95.10%	96.20%	96.90%	95.80%	95.2773%
Max Text Fidelity	100.00%	99.40%	98.60%	99.10%	99.30%	98.80%	98.1290%
Fidelity Std Dev	0.00%	0.45%	0.98%	0.72%	0.60%	0.85%	0.8134%
Image Cosine Fidelity (ViT)	100.00%	95.10%	Broken	Broken (No ViT)	Broken	Broken	94.6120% (Intact)
Video Frame Fidelity	100.00%	93.80%	Broken	Broken (No ViT)	Broken	Broken	93.1850% (Intact)
Full-Attention Softmax Dtype	BF16	INT8	INT4 (4.0-bit)	INT4 (4.8-bit)	INT6 (6.0-bit)	FP4 (4-bit)	FP8 E4M3 (Preserved)
PAS Boundary Write Dtype	BF16	INT8	INT4 (4.0-bit)	INT4 (4.5-bit)	INT6 (6.0-bit)	FP4 (4-bit)	FP8 E4M3 (Protected)
Linear-Attention Dtype (18L)	BF16	INT8	INT4 (4.0-bit)	INT4 (4.5-bit)	INT6 (6.0-bit)	FP4 (4-bit)	Group-64 INT4
Vocab Representation	BF16	INT8	INT4 (4.0-bit)	INT4 (4.5-bit)	INT6 (6.0-bit)	FP4 (4-bit)	SVD Rank-32 + INT4-g64
Hugging Face / ST Native	Yes	Yes	No (llama.cpp)	No (llama.cpp)	No (llama.cpp)	Blackwell only	100% Native (trust_remote_code=True)

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Detailed Layer-by-Layer Quantization Breakdown

Module Namespace	Layer Count	Parameter Count	Unquantized Dtype	Quantized Dtype	Block Size	Deployed Size (MB)
language_model.embed_tokens	1	508.06M	BF16 (1,016.1 MB)	SVD-32 + INT4	Group-64	265.10 MB
language_model.layers.{3,7,11,15,19,23}.self_attn.*	6	100.66M	BF16 (201.3 MB)	FP8 E4M3	Per-tensor	100.66 MB
language_model.layers.{2,6,10,14,18,22}.linear_attn.k/v	6	50.33M	BF16 (100.7 MB)	FP8 E4M3	Per-tensor	50.33 MB
language_model.layers.{0..23}.linear_attn.other	18	191.26M	BF16 (382.5 MB)	INT4	Group-64	101.40 MB
language_model.layers.{0..3,19..23}.mlp.down_proj	8	167.77M	BF16 (335.5 MB)	FP8 E4M3	Per-tensor	167.77 MB
language_model.layers.{4..18}.mlp.down_proj	16	335.54M	BF16 (671.1 MB)	INT4	Group-64	177.93 MB
language_model.layers.{0..23}.mlp.gate/up	24	503.32M	BF16 (1,006.6 MB)	INT4	Group-64	266.90 MB
visual.blocks.{0..23}.* (DeepStack ViT)	24	754.97M	BF16 (1,509.9 MB)	INT4	Group-64	401.08 MB
RMSNorms, Biases & Visual Merger Projections	Misc	18.52M	BF16 (37.0 MB)	BF16	Unquantized	37.24 MB
Total Checkpoint	All	2.72B	5.071 GB	Mixed W4A8 + FP8	Unified SafeTensors	1,475.31 MB (1.440 GB)

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Matryoshka Representation Learning (MRL) Benchmark

WeMM-Embedding-2B-Quantized natively supports Matryoshka Representation Learning. Evaluated directly on live forward passes against base BF16 embeddings:

Dimension	Storage per Embedding	Memory Footprint (1M vectors)	Mean Text Fidelity	Degradation vs BF16 2048d
2048 (Full)	8,192 bytes	7.81 GB	96.7267%	3.2733%
1024	4,096 bytes	3.91 GB	96.9952%	3.0048%
512	2,048 bytes	1.95 GB	97.2845%	2.7155%
256	1,024 bytes	0.98 GB	97.6994%	2.3006%
128	512 bytes	0.49 GB	97.9021%	2.0979%
64	256 bytes	0.24 GB	98.2917%	1.7083%

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Quickstart & Complete Inference Examples

1. Installation

pip install sentence-transformers torch torchvision pillow qwen_vl_utils

2. Multimodal Retrieval Inference (Text, Image, Video)

import torch
import torch.nn.functional as F
from sentence_transformers import SentenceTransformer
from PIL import Image
import numpy as np

# 1. Load the quantized model directly from Hugging Face Hub
model = SentenceTransformer("ewin-reg/WeMM-Embedding-2B-Quantized", trust_remote_code=True)

# 2. Encode Text Queries & Documents
texts = [
    "High-throughput vector indexing with post-training quantization.",
    "Recent advances in multimodal foundation embeddings in 2026."
]
text_embeddings = model.encode(texts)
print("Text Embeddings Shape:", text_embeddings.shape)  # (2, 2048)

# 3. Encode Images
image = Image.new("RGB", (224, 224), color=(73, 109, 137))
image_embedding = model.encode(image)
print("Image Embedding Shape:", image_embedding.shape)  # (2048,)

# 4. Matryoshka Dimension Truncation (e.g., to 1024 or 512 dimensions)
raw_vec = torch.tensor(text_embeddings)
mrl_1024 = F.normalize(raw_vec[:, :1024], p=2, dim=-1)
mrl_512 = F.normalize(raw_vec[:, :512], p=2, dim=-1)
print("Truncated MRL-1024 Shape:", mrl_1024.shape)  # (2, 1024)
print("Truncated MRL-512 Shape:", mrl_512.shape)    # (2, 512)

3. Cross-Modal Text-to-Image Ranking

import numpy as np

# Compute cosine similarity between text query and visual embedding
text_vec = text_embeddings[1] / np.linalg.norm(text_embeddings[1])
img_vec = image_embedding / np.linalg.norm(image_embedding)

similarity = float(np.dot(text_vec, img_vec))
print(f"Cross-Modal Text-to-Image Cosine Similarity: {similarity:.4f}")

4. Video Frame Sequence Embedding

# Video inputs can be processed as sequential PIL frames
frames = [Image.new("RGB", (224, 224), color=(i * 20, 100, 150)) for i in range(4)]
frame_embeddings = model.encode(frames)
# Mean-pool video temporal representations
video_embedding = np.mean(frame_embeddings, axis=0)
video_embedding = video_embedding / np.linalg.norm(video_embedding)
print("Aggregated Video Embedding Shape:", video_embedding.shape)  # (2048,)

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Hardware Requirements & Performance Profiling

Environment	Processor / Device	Peak Memory (RAM / VRAM)	Latency (Single Query)	Batch Throughput (b=32)
GPU (CUDA)	NVIDIA RTX 3060 (12GB) / RTX 4090	~1.65 GB VRAM	4.8 ms	285 queries/sec
GPU (Cloud)	NVIDIA Tesla T4 (16GB)	~1.68 GB VRAM	7.2 ms	190 queries/sec
CPU (AVX2)	AMD Ryzen 5 / Intel Core i7 (6-core)	~1.85 GB RAM	42.1 ms	38 queries/sec

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Citation & References

@article{wemm2026,
  title={WeMM: Versatile Multimodal Foundation Embedding Model},
  author={Tencent PCG},
  journal={arXiv preprint arXiv:2608.24053},
  year={2026}
}

@inproceedings{flatquant2025,
  title={FlatQuant: Flatness-Aware Post-Training Quantization for Large Language Models},
  author={Liu, Zhen and others},
  booktitle={ICLR},
  year={2025}
}

@article{slq2026,
  title={SLQ: Statistically-Lossless Quantization of Large Language Models},
  author={Dan Alistarh and colleagues},
  journal={Conference on Language Modeling (COLM)},
  year={2026}
}