Feature Extraction
sentence-transformers
Safetensors
English
fusion-embedding-connector
embeddings
multimodal
audio
retrieval
matryoshka
qwen3-vl
adapters
custom_code
Instructions to use EximiusLabs/fusion-embedding-2-2b-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use EximiusLabs/fusion-embedding-2-2b-preview with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("EximiusLabs/fusion-embedding-2-2b-preview", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
| """HF remote-code configuration for the fusion-embedding family. | |
| Lives on the model repos (EximiusLabs/fusion-embedding-1-2b-preview and | |
| EximiusLabs/fusion-embedding-2-2b-preview) next to modeling_fusion_embedding.py so the | |
| models load with plain transformers: | |
| from transformers import AutoModel | |
| model = AutoModel.from_pretrained( | |
| "EximiusLabs/fusion-embedding-1-2b-preview", trust_remote_code=True) | |
| The config carries the trained-connector dimensions plus the frozen-component repo | |
| names; the frozen Qwen3-VL-Embedding base and Qwen2.5-Omni audio tower are NOT part of | |
| this checkpoint — they are fetched from their own repositories at first use. | |
| """ | |
| from __future__ import annotations | |
| from transformers import PretrainedConfig | |
| class FusionEmbeddingConfig(PretrainedConfig): | |
| model_type = "fusion-embedding-connector" | |
| def __init__( | |
| self, | |
| d_audio: int = 3584, | |
| d_llm: int = 2048, | |
| n_query: int = 64, | |
| d_resampler: int = 384, | |
| resampler_depth: int = 6, | |
| resampler_heads: int = 8, | |
| resampler_ffn_mult: int = 4, | |
| resampler_dropout: float = 0.0, | |
| adapter_rank: int = 0, | |
| adapter_act: str = "silu", | |
| mrl_dims=(2048, 1536, 1024, 512, 256, 128, 64), | |
| mrl_default: int = 1024, | |
| audio_pad_id: int = 151654, | |
| eos_id: int = 151645, | |
| pad_id: int = 151643, | |
| audio_pad_token: str = "<|audio_pad|>", | |
| base_model: str = "Qwen/Qwen3-VL-Embedding-2B", | |
| audio_model: str = "Qwen/Qwen2.5-Omni-7B", | |
| max_text_tokens: int = 512, | |
| n_decoder_layers: int = 28, | |
| **kwargs, | |
| ): | |
| self.d_audio = d_audio | |
| self.d_llm = d_llm | |
| self.n_query = n_query | |
| self.d_resampler = d_resampler | |
| self.resampler_depth = resampler_depth | |
| self.resampler_heads = resampler_heads | |
| self.resampler_ffn_mult = resampler_ffn_mult | |
| self.resampler_dropout = resampler_dropout | |
| self.adapter_rank = adapter_rank or 0 | |
| self.adapter_act = adapter_act or "silu" | |
| self.mrl_dims = list(mrl_dims) | |
| self.mrl_default = mrl_default | |
| self.audio_pad_id = audio_pad_id | |
| self.eos_id = eos_id | |
| self.pad_id = pad_id | |
| self.audio_pad_token = audio_pad_token | |
| self.base_model = base_model | |
| self.audio_model = audio_model | |
| self.max_text_tokens = max_text_tokens | |
| self.n_decoder_layers = n_decoder_layers | |
| super().__init__(**kwargs) | |