multi-modal-embed-small

A compact multimodal embedding model that unifies text, image, and audio representations in a shared semantic space. Part of the MoM (Mixture of Models) family.

Model Description

multi-modal-embed-small is a lightweight multimodal encoder (~120M parameters) supporting:

• Text encoding via MiniLM-L6-v2 (22M params)
• Image encoding via SigLIP-base-patch16-512 (86M params)
• Audio encoding via Whisper-tiny encoder (8M params)
• Cross-modal fusion via 2-layer transformer attention
• 2DMSE: Two-Dimensional Matryoshka Sentence Embeddings for adaptive compute
• MRL: Matryoshka Representation Learning for flexible embedding dimensions

Key Features

Installation

pip install torch transformers pillow safetensors

Usage

Load Model

Two checkpoint formats are available:

• model.pt (932 MB) - PyTorch format
• model.safetensors (1.35 GB) - SafeTensors format

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import AutoModel, AutoTokenizer, SiglipModel, SiglipProcessor, WhisperModel, WhisperFeatureExtractor
from huggingface_hub import hf_hub_download

class MultiModalEmbedder(nn.Module):
    """Standalone multimodal embedder - no external dependencies."""

    def __init__(self):
        super().__init__()
        # Text encoder (384d, no projection needed)
        self.text_tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
        self.text_encoder = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")

        # Image encoder (768d -> 384d projection)
        self.image_processor = SiglipProcessor.from_pretrained("google/siglip-base-patch16-512")
        self.image_encoder = SiglipModel.from_pretrained("google/siglip-base-patch16-512").vision_model
        self.image_proj = nn.Linear(768, 384)

        # Audio encoder (384d, no projection needed)
        self.audio_processor = WhisperFeatureExtractor.from_pretrained("openai/whisper-tiny")
        self.audio_encoder = WhisperModel.from_pretrained("openai/whisper-tiny").encoder

    def encode_text(self, texts):
        if isinstance(texts, str):
            texts = [texts]
        inputs = self.text_tokenizer(texts, padding=True, truncation=True, return_tensors="pt")
        inputs = {k: v.to(next(self.parameters()).device) for k, v in inputs.items()}
        outputs = self.text_encoder(**inputs)
        embeddings = outputs.last_hidden_state.mean(dim=1)  # Mean pooling
        return F.normalize(embeddings, p=2, dim=-1)

    def encode_image(self, images):
        inputs = self.image_processor(images=images, return_tensors="pt")
        inputs = {k: v.to(next(self.parameters()).device) for k, v in inputs.items()}
        outputs = self.image_encoder(**inputs)
        embeddings = outputs.pooler_output
        embeddings = self.image_proj(embeddings)  # 768 -> 384
        return F.normalize(embeddings, p=2, dim=-1)

    def encode_audio(self, waveform):
        # waveform: numpy array or tensor at 16kHz
        if isinstance(waveform, torch.Tensor):
            waveform = waveform.squeeze().numpy()
        inputs = self.audio_processor(waveform, sampling_rate=16000, return_tensors="pt")
        inputs = {k: v.to(next(self.parameters()).device) for k, v in inputs.items()}
        outputs = self.audio_encoder(**inputs)
        embeddings = outputs.last_hidden_state.mean(dim=1)  # Mean pooling
        return F.normalize(embeddings, p=2, dim=-1)

# Load model
model = MultiModalEmbedder()

# Download and load trained weights
checkpoint_path = hf_hub_download(
    repo_id="llm-semantic-router/multi-modal-embed-small",
    filename="model.pt"
)
state_dict = torch.load(checkpoint_path, map_location="cpu", weights_only=False)

# Load text encoder weights
model.text_encoder.load_state_dict({
    k.replace("text_encoder.encoder.", ""): v
    for k, v in state_dict.items()
    if k.startswith("text_encoder.encoder.")
})

# Load image encoder and projection weights
model.image_encoder.load_state_dict({
    k.replace("image_encoder.vision_encoder.", ""): v
    for k, v in state_dict.items()
    if k.startswith("image_encoder.vision_encoder.")
})
model.image_proj.load_state_dict({
    k.replace("image_encoder.projection.", ""): v
    for k, v in state_dict.items()
    if k.startswith("image_encoder.projection.")
})

# Load audio encoder weights
model.audio_encoder.load_state_dict({
    k.replace("audio_encoder.encoder.", ""): v
    for k, v in state_dict.items()
    if k.startswith("audio_encoder.encoder.")
})

model.eval()
print("Model loaded successfully!")

Text Embedding

import torch.nn.functional as F

# Single text
text_embedding = model.encode_text("A photo of a cat")  # Shape: [1, 384]

# Batch of texts
texts = ["A fluffy orange cat", "A golden retriever dog", "A red sports car"]
text_embeddings = model.encode_text(texts)  # Shape: [3, 384]

# Compute similarity
similarities = F.cosine_similarity(text_embeddings[0:1], text_embeddings[1:], dim=-1)
print(f"Cat vs Dog: {similarities[0]:.3f}")
print(f"Cat vs Car: {similarities[1]:.3f}")

Image Embedding

from PIL import Image
import requests
from io import BytesIO

# Load image
url = "https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Cat03.jpg/1200px-Cat03.jpg"
image = Image.open(BytesIO(requests.get(url).content)).convert('RGB')

# Get embedding
image_embedding = model.encode_image(image)  # Shape: [1, 384]

Audio Embedding

import torchaudio

# Load audio (16kHz)
waveform, s