EximiusLabs
/

fusion-embedding-2-2b-preview

@@ -1,226 +1,230 @@
----
-license: cc-by-nc-4.0
-language:
-  - en
-pipeline_tag: feature-extraction
-tags:
-  - embeddings
-  - multimodal
-  - audio
-  - retrieval
-  - matryoshka
-  - qwen3-vl
-  - adapters
-base_model: Qwen/Qwen3-VL-Embedding-2B
----
-# fusion-embedding-2-2b-preview
-<div align="center">
-[![Python](https://img.shields.io/badge/python-3.11+-blue.svg)](https://github.com/Eximius-Labs/fusion-embedding-1)
-[![PyTorch](https://img.shields.io/badge/PyTorch-2.x-ee4c2c.svg)](https://github.com/Eximius-Labs/fusion-embedding-1)
-[![Weights](https://img.shields.io/badge/weights-CC--BY--NC--4.0-green.svg)](#license)
-[![Status](https://img.shields.io/badge/status-research%20preview%20v0.1-orange.svg)](#)
-[![Code](https://img.shields.io/badge/code-GitHub-black.svg)](https://github.com/Eximius-Labs/fusion-embedding-1)
-</div>
-`fusion-embedding-2-2b-preview` is the second generation of Eximius Labs' unified
-multimodal embedding models: **text, images, video, and audio in one vector space**.
-It extends the first generation with modality-gated deep adapters — in-layer audio
-capacity added to a byte-frozen base. For the first-generation architecture, see
-[fusion-embedding-1-2b-preview](https://huggingface.co/EximiusLabs/fusion-embedding-1-2b-preview)
-(that line is final at v0.3).
-[GitHub](https://github.com/Eximius-Labs/fusion-embedding-1) | [fusion-embedding-1](https://huggingface.co/EximiusLabs/fusion-embedding-1-2b-preview) | Technical report: in preparation
-## Model Overview
-`fusion-embedding-2-2b-preview` embeds all four modalities with a
-[Qwen3-VL-Embedding-2B](https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B) base that is
-**byte-identical to its original release** — its text, image, and video behaviour (and
-benchmark scores) carry over exactly. Audio is added by training 60.6M parameters
-(~2.3% of the stack): a perceiver-resampler that translates frozen
-[Qwen2.5-Omni](https://huggingface.co/Qwen/Qwen2.5-Omni-7B) audio-tower features into
-the base's input space, and — new in this generation — **28 gated adapters** (44.2M)
-that give the frozen language model in-layer capacity to process audio. The adapters
-are active only while encoding audio; every other forward pass returns the frozen
-layers' output untouched, so the invariance is bitwise, not approximate
-(`base_drift == 0` is asserted on every training run, and this model reproduces the
-base's text→image retrieval scores to four decimal places). Trained on 518K
-audio–caption pairs with a full-corpus frozen-text negative bank, it leads every
-unified embedding model we measured on audio↔text retrieval — ahead of ImageBind,
-LanguageBind, and Gemini Embedding 2 in both directions — and improves on
-fusion-embedding-1 v0.3 in 9 of 12 measured cells, with the largest gains in
-text→audio search. Audio↔image alignment is emergent (zero audio–image pairs in
-training).
-| Feature | Value |
-| --- | --- |
-| Parameters | ~2.06B frozen base + 640M frozen audio tower; **60.6M trained** |
-| Modalities | text, image, video, audio |
-| Supported tasks | `retrieval` (all modality pairs), `zero-shot classification` |
-| Max input | 254 text tokens · 30 s audio per window (up to 8 windows) |
-| Embedding dimension | 2048 |
-| Matryoshka dimensions | 64, 128, 256, 512, 1024, 1536, 2048 |
-| Pooling strategy | Last-token pooling |
-| Base model | Qwen/Qwen3-VL-Embedding-2B (byte-frozen) |
-| Audio tower | Qwen/Qwen2.5-Omni-7B audio encoder (frozen) |
-| Trained components | FusionResampler 16.4M + 28× gated adapters 44.2M |
-| Distribution | ~250 MB trained components; frozen towers download from their original repos |
-## Training and Evaluation
-Contrastive training (InfoNCE over the Matryoshka ladder, symmetric) against the
-frozen base's native chat-template text embeddings: 518,183 audio–caption pairs from
-six sources (73,716 clips with content-free metadata excluded), a full-corpus
-frozen-text negative bank, soft labels 0.3, false-negative masking 0.98, bf16, 3,900
-steps at effective batch 1,024, then a 400-step in-domain fine-tune on the AudioCaps
-train split. All evaluation-set audio (Clotho, ESC-50, UrbanSound8K, VGGSound,
-AudioCaps test/val) is excluded from training by ID blacklists at ingestion. A
-technical report is in preparation.
-All numbers below use the release protocol (bf16 base precision, native chat-template
-text). Bold marks the better value per row/column.
-<details open>
-  <summary><b>Versus fusion-embedding-1 v0.3</b></summary>
-| Board / direction | fusion-embedding-1 v0.3 | fusion-embedding-2 (this repo) |
-|---|---|---|
-| AudioCaps A→T R@1 | **0.332** | 0.302 |
-| AudioCaps A→T R@10 | 0.741 | **0.743** |
-| AudioCaps T→A R@1 | — | **0.292** |
-| AudioCaps T→A R@10 | 0.746 | **0.775** |
-| Clotho (zero-shot) A→T R@1 | **0.135** | 0.127 |
-| Clotho (zero-shot) A→T R@10 | **0.433** | 0.421 |
-| Clotho (zero-shot) T→A R@1 | ~0.13 | **0.151** |
-| Clotho (zero-shot) T→A R@10 | 0.460 | **0.482** |
-| VGGSound audio→text R@1 | **0.213** | 0.211 |
-| VGGSound audio→text R@10 | 0.625 | **0.665** |
-| VGGSound text→audio R@1 | 0.213 | **0.266** |
-| VGGSound text→audio R@10 | 0.645 | **0.681** |
-| VGGSound audio→image R@10 (emergent) | **0.407** | 0.392 |
-fusion-embedding-2 takes the majority of cells, with its largest gains in the
-text→audio direction (searching audio with a text query) and on the cross-modal
-audio↔text pair. fusion-embedding-1 v0.3 retains the AudioCaps and Clotho A→T R@1
-cells and a ~1.5-point edge on emergent audio→image at this fine-tuned operating
-point; the pre-fine-tune fusion-embedding-2 checkpoint scores 0.443 on that cell — the
-project record — and may be released separately as the emergent-alignment operating
-point.
-</details>
-<details>
-  <summary><b>Cross-modal retrieval — versus unified embedding models</b> (VGGSound-AV, 696 pairs, chance R@10 = 0.014)</summary>
-R@10 shown as audio-side → other / other → audio-side:
-| Model | audio↔image | audio↔text | text↔image |
-|---|---|---|---|
-| ImageBind-Huge | **0.718 / 0.720** | 0.404 / 0.348 | 0.243 / 0.282 |
-| LanguageBind | 0.365 / 0.415 | 0.547 / 0.331 | 0.221 / 0.283 |
-| Gemini Embedding 2 (API, 2026-07-09) | 0.312 / 0.316 | 0.379 / 0.374 | 0.273 / **0.366** |
-| fusion-embedding-1-2b-preview v0.3 | 0.407 / 0.428 | 0.625 / 0.645 | **0.331** / 0.319 |
-| **fusion-embedding-2-2b-preview** | 0.392 / 0.430 | **0.665 / 0.681** | **0.331** / 0.319 |
-ImageBind trains directly on audio–image pairs, so that pair is its supervised
-direction; its audio–text alignment is emergent. LanguageBind trains audio against
-language; its audio↔image is emergent. Both fusion-embedding generations train on
-audio–text only; their audio–image alignment is emergent. All models evaluated with
-identical clips, frames, and scoring, using the released imagebind_huge checkpoint and
-revision-pinned LanguageBind checkpoints. Gemini Embedding 2 is Google's natively
-multimodal embedding API, evaluated at its documented default invocation on the date
-shown; API models may change after that date. fusion-embedding-2's text↔image cells
-are identical to fusion-embedding-1's by construction — text and images never touch
-the trained components — and this is verified: its own readout run reproduces
-fusion-embedding-1 v0.3's text→image scores to four decimal places.
-</details>
-<details>
-  <summary><b>Audio–text retrieval — versus specialist CLAP models</b></summary>
-Specialist CLAP models fine-tune their text towers on audio captions — the direct
-trade this architecture declines in order to keep one shared space for all four
-modalities. They remain ahead on the audio-caption boards (e.g., AudioCaps T→A R@1:
-M2D-CLAP 41.4 vs 29.2 here); this model family is the strongest option we measured
-when one model must serve text, images, video, and audio together. See the
-[fusion-embedding-1 card](https://huggingface.co/EximiusLabs/fusion-embedding-1-2b-preview)
-for the full CLAP comparison tables; fusion-embedding-2 improves on fusion-embedding-1
-in the text→audio direction on every board.
-</details>
-## Usage
-<details>
-  <summary>Requirements</summary>
-- `fusion_embedding` package: `pip install git+https://github.com/Eximius-Labs/fusion-embedding-1`
-- `transformers>=4.46`, `torch` (CUDA), `torchvision`, `pillow`, `soundfile`, `librosa`
-- ~14 GB GPU memory at bf16
-</details>
-<details open>
-  <summary>via <code>inference.py</code> (this repository)</summary>
-```python
-from inference import FusionEmbedder
-fe = FusionEmbedder.from_pretrained(
-    "EximiusLabs/fusion-embedding-2-2b-preview",
-    revision="v0.1-preview",   # pin a tag if you build on this model
-)
-a = fe.embed_audio("dog.wav")            # audio file or (array, sr=...)
-t = fe.embed_text("a dog barks")         # uses the base's native chat template
-i = fe.embed_image("dog.jpg")            # PIL image or path
-print((a @ t).item(), (a @ i).item())    # cosine similarities in the shared space
-# Matryoshka: pass dim= for smaller embeddings (64..2048)
-t_small = fe.embed_text("a dog barks", dim=256)
-```
-The checkpoint contains the gated adapters and the loader refuses to run without them —
-an adapter checkpoint can never be silently executed as the first-generation
-architecture. All inputs use the base model's chat-template format; embedding quality
-is sensitive to this formatting, so use the templates provided by `FusionEmbedder`
-rather than constructing your own.
-</details>
-<details>
-  <summary>Cross-modal ranking tip</summary>
-When ranking a gallery of one modality against queries of another, per-modality
-mean-centering of the gallery improves cross-modal recall by roughly two points across
-modality pairs:
-```python
-gallery = FusionEmbedder.center(gallery_embeddings)
-```
-</details>
-## License
-Code is Apache-2.0 ([GitHub](https://github.com/Eximius-Labs/fusion-embedding-1));
-model weights in this repository are **CC BY-NC 4.0** (research preview). The frozen
-base and audio tower retain their original licenses.
-## Citation
-```bibtex
-@software{fusion_embedding_2_2026,
-  title  = {Fusion Embedding 2: Modality-Gated Deep Adapters for a
-            Unified Text, Image, Video, and Audio Embedding Space},
-  author = {Tonmoy, Abdul Basit},
-  year   = {2026},
-  url    = {https://huggingface.co/EximiusLabs/fusion-embedding-2-2b-preview}
-}
-```

+---
+license: cc-by-nc-4.0
+language:
+  - en
+pipeline_tag: feature-extraction
+tags:
+  - embeddings
+  - multimodal
+  - audio
+  - retrieval
+  - matryoshka
+  - qwen3-vl
+  - adapters
+base_model: Qwen/Qwen3-VL-Embedding-2B
+---
+# fusion-embedding-2-2b-preview
+<div align="center">
+[![Python](https://img.shields.io/badge/python-3.11+-blue.svg)](https://github.com/Eximius-Labs/fusion-embedding-1)
+[![PyTorch](https://img.shields.io/badge/PyTorch-2.x-ee4c2c.svg)](https://github.com/Eximius-Labs/fusion-embedding-1)
+[![Weights](https://img.shields.io/badge/weights-CC--BY--NC--4.0-green.svg)](#license)
+[![Status](https://img.shields.io/badge/status-research%20preview%20v0.1-orange.svg)](#)
+[![Code](https://img.shields.io/badge/code-GitHub-black.svg)](https://github.com/Eximius-Labs/fusion-embedding-1)
+</div>
+`fusion-embedding-2-2b-preview` is the second generation of Eximius Labs' unified
+multimodal embedding models: **text, images, video, and audio in one vector space**.
+It extends the first generation with modality-gated deep adapters — in-layer audio
+capacity added to a byte-frozen base. For the first-generation architecture, see
+[fusion-embedding-1-2b-preview](https://huggingface.co/EximiusLabs/fusion-embedding-1-2b-preview)
+(that line is final at v0.3).
+[GitHub](https://github.com/Eximius-Labs/fusion-embedding-1) | [fusion-embedding-1](https://huggingface.co/EximiusLabs/fusion-embedding-1-2b-preview) | Technical report: in preparation
+## Model Overview
+<p align="center">
+<img src="assets/fe2_model_overview.png" alt="fusion-embedding-2 architecture: frozen Qwen3-VL-Embedding base with modality-gated adapters inside; frozen audio tower and trained FusionResampler on the audio branch; one shared embedding space" width="820px">
+</p>
+`fusion-embedding-2-2b-preview` embeds all four modalities with a
+[Qwen3-VL-Embedding-2B](https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B) base that is
+**byte-identical to its original release** — its text, image, and video behaviour (and
+benchmark scores) carry over exactly. Audio is added by training 60.6M parameters
+(~2.3% of the stack): a perceiver-resampler that translates frozen
+[Qwen2.5-Omni](https://huggingface.co/Qwen/Qwen2.5-Omni-7B) audio-tower features into
+the base's input space, and — new in this generation — **28 gated adapters** (44.2M)
+that give the frozen language model in-layer capacity to process audio. The adapters
+are active only while encoding audio; every other forward pass returns the frozen
+layers' output untouched, so the invariance is bitwise, not approximate
+(`base_drift == 0` is asserted on every training run, and this model reproduces the
+base's text→image retrieval scores to four decimal places). Trained on 518K
+audio–caption pairs with a full-corpus frozen-text negative bank, it leads every
+unified embedding model we measured on audio↔text retrieval — ahead of ImageBind,
+LanguageBind, and Gemini Embedding 2 in both directions — and improves on
+fusion-embedding-1 v0.3 in 9 of 12 measured cells, with the largest gains in
+text→audio search. Audio↔image alignment is emergent (zero audio–image pairs in
+training).
+| Feature | Value |
+| --- | --- |
+| Parameters | ~2.06B frozen base + 640M frozen audio tower; **60.6M trained** |
+| Modalities | text, image, video, audio |
+| Supported tasks | `retrieval` (all modality pairs), `zero-shot classification` |
+| Max input | 254 text tokens · 30 s audio per window (up to 8 windows) |
+| Embedding dimension | 2048 |
+| Matryoshka dimensions | 64, 128, 256, 512, 1024, 1536, 2048 |
+| Pooling strategy | Last-token pooling |
+| Base model | Qwen/Qwen3-VL-Embedding-2B (byte-frozen) |
+| Audio tower | Qwen/Qwen2.5-Omni-7B audio encoder (frozen) |
+| Trained components | FusionResampler 16.4M + 28× gated adapters 44.2M |
+| Distribution | ~250 MB trained components; frozen towers download from their original repos |
+## Training and Evaluation
+Contrastive training (InfoNCE over the Matryoshka ladder, symmetric) against the
+frozen base's native chat-template text embeddings: 518,183 audio–caption pairs from
+six sources (73,716 clips with content-free metadata excluded), a full-corpus
+frozen-text negative bank, soft labels 0.3, false-negative masking 0.98, bf16, 3,900
+steps at effective batch 1,024, then a 400-step in-domain fine-tune on the AudioCaps
+train split. All evaluation-set audio (Clotho, ESC-50, UrbanSound8K, VGGSound,
+AudioCaps test/val) is excluded from training by ID blacklists at ingestion. A
+technical report is in preparation.
+All numbers below use the release protocol (bf16 base precision, native chat-template
+text). Bold marks the better value per row/column.
+<details open>
+  <summary><b>Versus fusion-embedding-1 v0.3</b></summary>
+| Board / direction | fusion-embedding-1 v0.3 | fusion-embedding-2 (this repo) |
+|---|---|---|
+| AudioCaps A→T R@1 | **0.332** | 0.302 |
+| AudioCaps A→T R@10 | 0.741 | **0.743** |
+| AudioCaps T→A R@1 | — | **0.292** |
+| AudioCaps T→A R@10 | 0.746 | **0.775** |
+| Clotho (zero-shot) A→T R@1 | **0.135** | 0.127 |
+| Clotho (zero-shot) A→T R@10 | **0.433** | 0.421 |
+| Clotho (zero-shot) T→A R@1 | ~0.13 | **0.151** |
+| Clotho (zero-shot) T→A R@10 | 0.460 | **0.482** |
+| VGGSound audio→text R@1 | **0.213** | 0.211 |
+| VGGSound audio→text R@10 | 0.625 | **0.665** |
+| VGGSound text→audio R@1 | 0.213 | **0.266** |
+| VGGSound text→audio R@10 | 0.645 | **0.681** |
+| VGGSound audio→image R@10 (emergent) | **0.407** | 0.392 |
+fusion-embedding-2 takes the majority of cells, with its largest gains in the
+text→audio direction (searching audio with a text query) and on the cross-modal
+audio↔text pair. fusion-embedding-1 v0.3 retains the AudioCaps and Clotho A→T R@1
+cells and a ~1.5-point edge on emergent audio→image at this fine-tuned operating
+point; the pre-fine-tune fusion-embedding-2 checkpoint scores 0.443 on that cell — the
+project record — and may be released separately as the emergent-alignment operating
+point.
+</details>
+<details>
+  <summary><b>Cross-modal retrieval — versus unified embedding models</b> (VGGSound-AV, 696 pairs, chance R@10 = 0.014)</summary>
+R@10 shown as audio-side → other / other → audio-side:
+| Model | audio↔image | audio↔text | text↔image |
+|---|---|---|---|
+| ImageBind-Huge | **0.718 / 0.720** | 0.404 / 0.348 | 0.243 / 0.282 |
+| LanguageBind | 0.365 / 0.415 | 0.547 / 0.331 | 0.221 / 0.283 |
+| Gemini Embedding 2 (API, 2026-07-09) | 0.312 / 0.316 | 0.379 / 0.374 | 0.273 / **0.366** |
+| fusion-embedding-1-2b-preview v0.3 | 0.407 / 0.428 | 0.625 / 0.645 | **0.331** / 0.319 |
+| **fusion-embedding-2-2b-preview** | 0.392 / 0.430 | **0.665 / 0.681** | **0.331** / 0.319 |
+ImageBind trains directly on audio–image pairs, so that pair is its supervised
+direction; its audio–text alignment is emergent. LanguageBind trains audio against
+language; its audio↔image is emergent. Both fusion-embedding generations train on
+audio–text only; their audio–image alignment is emergent. All models evaluated with
+identical clips, frames, and scoring, using the released imagebind_huge checkpoint and
+revision-pinned LanguageBind checkpoints. Gemini Embedding 2 is Google's natively
+multimodal embedding API, evaluated at its documented default invocation on the date
+shown; API models may change after that date. fusion-embedding-2's text↔image cells
+are identical to fusion-embedding-1's by construction — text and images never touch
+the trained components — and this is verified: its own readout run reproduces
+fusion-embedding-1 v0.3's text→image scores to four decimal places.
+</details>
+<details>
+  <summary><b>Audio–text retrieval — versus specialist CLAP models</b></summary>
+Specialist CLAP models fine-tune their text towers on audio captions — the direct
+trade this architecture declines in order to keep one shared space for all four
+modalities. They remain ahead on the audio-caption boards (e.g., AudioCaps T→A R@1:
+M2D-CLAP 41.4 vs 29.2 here); this model family is the strongest option we measured
+when one model must serve text, images, video, and audio together. See the
+[fusion-embedding-1 card](https://huggingface.co/EximiusLabs/fusion-embedding-1-2b-preview)
+for the full CLAP comparison tables; fusion-embedding-2 improves on fusion-embedding-1
+in the text→audio direction on every board.
+</details>
+## Usage
+<details>
+  <summary>Requirements</summary>
+- `fusion_embedding` package: `pip install git+https://github.com/Eximius-Labs/fusion-embedding-1`
+- `transformers>=4.46`, `torch` (CUDA), `torchvision`, `pillow`, `soundfile`, `librosa`
+- ~14 GB GPU memory at bf16
+</details>
+<details open>
+  <summary>via <code>inference.py</code> (this repository)</summary>
+```python
+from inference import FusionEmbedder
+fe = FusionEmbedder.from_pretrained(
+    "EximiusLabs/fusion-embedding-2-2b-preview",
+    revision="v0.1-preview",   # pin a tag if you build on this model
+)
+a = fe.embed_audio("dog.wav")            # audio file or (array, sr=...)
+t = fe.embed_text("a dog barks")         # uses the base's native chat template
+i = fe.embed_image("dog.jpg")            # PIL image or path
+print((a @ t).item(), (a @ i).item())    # cosine similarities in the shared space
+# Matryoshka: pass dim= for smaller embeddings (64..2048)
+t_small = fe.embed_text("a dog barks", dim=256)
+```
+The checkpoint contains the gated adapters and the loader refuses to run without them —
+an adapter checkpoint can never be silently executed as the first-generation
+architecture. All inputs use the base model's chat-template format; embedding quality
+is sensitive to this formatting, so use the templates provided by `FusionEmbedder`
+rather than constructing your own.
+</details>
+<details>
+  <summary>Cross-modal ranking tip</summary>
+When ranking a gallery of one modality against queries of another, per-modality
+mean-centering of the gallery improves cross-modal recall by roughly two points across
+modality pairs:
+```python
+gallery = FusionEmbedder.center(gallery_embeddings)
+```
+</details>
+## License
+Code is Apache-2.0 ([GitHub](https://github.com/Eximius-Labs/fusion-embedding-1));
+model weights in this repository are **CC BY-NC 4.0** (research preview). The frozen
+base and audio tower retain their original licenses.
+## Citation
+```bibtex
+@software{fusion_embedding_2_2026,
+  title  = {Fusion Embedding 2: Modality-Gated Deep Adapters for a
+            Unified Text, Image, Video, and Audio Embedding Space},
+  author = {Tonmoy, Abdul Basit},
+  year   = {2026},
+  url    = {https://huggingface.co/EximiusLabs/fusion-embedding-2-2b-preview}
+}
+```