Card: fix line endings (clean LF; restore YAML frontmatter parsing)

README.md

@@ -1,269 +1,538 @@
----
-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
----
-
-<p align="center">
-  <img src="assets/fusion-embedding-2-banner.png" alt="Fusion Embedding 2 (2B-Preview) — Eximius Labs" width="100%">
-</p>
-
-# 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)
-[![PyTorch](https://img.shields.io/badge/PyTorch-2.x-ee4c2c.svg)](https://github.com/Eximius-Labs/fusion-embedding)
-[![Weights](https://img.shields.io/badge/weights-CC--BY--NC--4.0-green.svg)](#license)
-[![Status](https://img.shields.io/badge/status-research%20preview%20v0.3-orange.svg)](#)
-[![Paper](https://img.shields.io/badge/paper-arXiv%3A2607.18666-b31b1b.svg)](https://arxiv.org/abs/2607.18666)
-[![Code](https://img.shields.io/badge/code-GitHub-black.svg)](https://github.com/Eximius-Labs/fusion-embedding)
-[![Deploy on RunPod](https://api.runpod.io/badge/Eximius-Labs/fusion-embedding)](https://www.runpod.io/console/hub/Eximius-Labs/fusion-embedding)
-
-</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**.
-Audio is added to a byte-frozen vision-language base through modality-gated deep
-adapters, so gaining audio never changes a single existing text, image, or video
-vector. 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) | Technical report: [arXiv:2607.18666](https://arxiv.org/abs/2607.18666)
-
-## 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 on 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
-its benchmark scores, carry over exactly. Audio is added by training 60.6M parameters
-(about 2.3% of the stack): a perceiver-resampler that maps frozen
-[Qwen2.5-Omni](https://huggingface.co/Qwen/Qwen2.5-Omni-7B) audio-tower features into
-the base's input space, plus **28 gated adapters** (44.2M) that give the frozen
-language model in-layer capacity for audio. The adapters run only while audio is being
-encoded; every other forward pass returns the frozen layers' output untouched, so the
-invariance is bitwise, not approximate. Adding audio never invalidates an index you
-have already built.
-
-This v0.3 release ships two revisions of the same model. The default is tuned for
-audio-text retrieval; a keyword-tuned revision is available for spoken-command use.
-
-## Versions in this release
-
-| Revision | Use it for | AudioCaps a2t R@10 | SpeechCommands |
-| --- | --- | --- | --- |
-| **`v0.3-preview`** (default) | general audio-text retrieval, RAG, clustering | **0.785** | 0.894 |
-| `v0.3-kw-preview` | spoken-keyword and command retrieval | 0.749 | **0.929** |
-
-The default (`v0.3-preview`) is the recommended model for almost all use. The
-keyword-tuned revision trades audio-text retrieval quality for higher zero-shot
-keyword-spotting accuracy; use it only when spoken-command matching is the priority,
-and use the default otherwise.
-
-## Highlights (v0.3-preview)
-
-| Feature | Value |
-| --- | --- |
-| Parameters | ~2.06B frozen base + 640M frozen audio tower; **60.6M trained** |
-| Modalities | text, image, video, audio |
-| AudioCaps 883 (audio-to-text) | R@1 0.333 · R@5 0.646 · R@10 **0.785** · mAP@10 0.255 |
-| AudioCaps 883 (text-to-audio) | R@1 0.297 · R@5 0.653 · R@10 0.782 · mAP@10 0.445 |
-| MAEB(audio-only), 19 tasks | mean **0.454**; leads the board on IEMOCAP speaker-gender (0.938) |
-| Non-audio preservation | text, image, video outputs bit-for-bit the base model's |
-| Embedding dimension | 2048 (Matryoshka: 64, 128, 256, 512, 1024, 1536, 2048) |
-| Base model | Qwen/Qwen3-VL-Embedding-2B (byte-frozen) |
-| Audio tower | Qwen/Qwen2.5-Omni-7B audio encoder (frozen) |
-
-## Evaluation
-
-<details open>
-  <summary>AudioCaps retrieval (883-clip test, min-rank over references)</summary>
-
-| Model | a2t R@10 | t2a R@10 |
-| --- | --- | --- |
-| fusion-embedding-2 v0.2 | 0.743 | 0.775 |
-| **v0.3-preview** | **0.785** | 0.782 |
-| v0.3-kw-preview | 0.749 | 0.771 |
-
-The v0.3 default improves audio-to-text retrieval by 4.2 points over v0.2.
-
-</details>
-
-<details>
-  <summary>MAEB(audio-only), full 19-task board — v0.3-preview</summary>
-
-Mean 0.4536. Scores are the main metric per task.
-
-| Task | Score |
-| --- | --- |
-| IEMOCAPGender | 0.938 |
-| BeijingOpera | 0.924 |
-| NMSQAPairClassification | 0.794 |
-| GTZANAudioReranking | 0.717 |
-| JamAltArtistA2ARetrieval | 0.687 |
-| GTZANGenre | 0.644 |
-| VoxPopuliLanguageID | 0.616 |
-| MInDS14 | 0.578 |
-| CREMADPairClassification | 0.543 |
-| MridinghamTonic | 0.540 |
-| VoxPopuliAccentPairClassification | 0.509 |
-| CREMA_D | 0.277 |
-| VoxCelebSA | 0.273 |
-| BirdCLEF | 0.184 |
-| CommonLanguageAgeDetection | 0.171 |
-| SIBFLEURS | 0.115 |
-| VoxPopuliGenderClustering | 0.079 |
-| VehicleSoundClustering | 0.025 |
-| CREMA_DClustering | 0.006 |
-
-IEMOCAP speaker-gender classification is the strongest cell relative to the field.
-Clustering tasks are the weakest and are a target for the next release.
-
-</details>
-
-<details>
-  <summary>Non-audio preservation and other modalities</summary>
-
-Text, image, and video outputs are bit-for-bit identical to the frozen
-Qwen3-VL-Embedding-2B base (the adapter gate is closed on those inputs). Their
-benchmark scores are therefore the base model's own, unchanged by anything added for
-audio. Audio-to-image retrieval is emergent: the model is trained on audio-text pairs
-only, with no audio-image supervision.
-
-</details>
-
-## Notes and limitations
-
-- **Emotion recognition regressed** relative to v0.2. On RAVDESS zero-shot emotion the
-  score is 0.21 (v0.2: 0.35). The v0.3 training mix improved spoken-content
-  understanding at the cost of vocal-prosody sensitivity. If speech-emotion is central
-  to your use, evaluate before adopting.
-- One evaluation cell, `CommonLanguageAgeDetection`, derives from Common Voice, which is
-  part of the training data. It scores low (0.171) and does not inflate the reported
-  mean, but the potential overlap is noted for completeness.
-- These are research previews under CC-BY-NC-4.0.
-
-## Usage
-
-<details>
-  <summary>Requirements</summary>
-
-- `fusion_embedding` package: `pip install git+https://github.com/Eximius-Labs/fusion-embedding`
-- `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="v0.3-preview",           # default, retrieval-tuned
-    # revision="v0.3-kw-preview",      # keyword-tuned alternative
-)
-
-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.
-All inputs use the base model's chat-template format; embedding quality is sensitive to
-this formatting, so use the templates provided by `FusionEmbedder`.
-
-</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:
-
-```python
-gallery = FusionEmbedder.center(gallery_embeddings)
-```
-
-</details>
-
-## Changelog
-
-- **v0.3-preview** (default): retrieval-tuned flagship. AudioCaps a2t R@10 0.785
-  (+0.042 over v0.2); MAEB(audio-only) mean 0.454; leads IEMOCAP speaker-gender.
-  RAVDESS emotion regressed to 0.21.
-- **v0.3-kw-preview**: keyword-tuned revision. SpeechCommands zero-shot 0.929;
-  AudioCaps a2t R@10 0.749.
-- **v0.2-preview**: AudioCaps 2.0 fine-tune. a2t R@10 0.743.
-- **v0.1-preview**: first modality-gated-adapter release.
-
-## Deploy on RunPod
-
-[![Deploy on RunPod](https://api.runpod.io/badge/Eximius-Labs/fusion-embedding)](https://www.runpod.io/console/hub/Eximius-Labs/fusion-embedding)
-
-One-click deploy the endpoint from the
-[RunPod Hub](https://www.runpod.io/console/hub/Eximius-Labs/fusion-embedding) (serverless,
-scales to zero when idle). Once it is running, call it:
-
-```bash
-curl -s https://api.runpod.ai/v2/<ENDPOINT_ID>/runsync   -H "Authorization: Bearer $RUNPOD_API_KEY"   -H "Content-Type: application/json"   -d '{"input": {"text": "a dog on a beach"}}'
-```
-
-The endpoint embeds all four modalities. Swap `text` for `image`, `video`, or `audio`
-(the value is an https URL, a data URI, or base64). Audio needs the audio tower, so set
-`FE2_ENABLE_AUDIO=1` on the endpoint and use a 24 GB or larger GPU. Returns 1024-d
-embeddings; add `"dim": 512` to truncate.
-
-## License
-
-Weights (all revisions): **CC-BY-NC-4.0** (research preview). The code is Apache-2.0.
-The frozen base and audio tower retain their original licenses.
-
-## Citation
-
-Model:
-
-```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}
-}
-```
-
-Technical report:
-
-```bibtex
-@article{tonmoy2026fusion,
-  title   = {Fusion Embedding: A Unified Embedding Space for Text, Image,
-             Video, and Audio},
-  author  = {Tonmoy, Abdul Basit and Hoque, Kazi Fardinul and
-             Arham, Md. Shahrier Islam and Luthra, Arman},
-  journal = {arXiv preprint arXiv:2607.18666},
-  year    = {2026}
-}
-```
+---
+
+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
+
+---
+
+
+
+<p align="center">
+
+  <img src="assets/fusion-embedding-2-banner.png" alt="Fusion Embedding 2 (2B-Preview) — Eximius Labs" width="100%">
+
+</p>
+
+
+
+# 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)
+
+[![PyTorch](https://img.shields.io/badge/PyTorch-2.x-ee4c2c.svg)](https://github.com/Eximius-Labs/fusion-embedding)
+
+[![Weights](https://img.shields.io/badge/weights-CC--BY--NC--4.0-green.svg)](#license)
+
+[![Status](https://img.shields.io/badge/status-research%20preview%20v0.3-orange.svg)](#)
+
+[![Paper](https://img.shields.io/badge/paper-arXiv%3A2607.18666-b31b1b.svg)](https://arxiv.org/abs/2607.18666)
+
+[![Code](https://img.shields.io/badge/code-GitHub-black.svg)](https://github.com/Eximius-Labs/fusion-embedding)
+
+[![Deploy on RunPod](https://api.runpod.io/badge/Eximius-Labs/fusion-embedding)](https://www.runpod.io/console/hub/Eximius-Labs/fusion-embedding)
+
+
+
+</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**.
+
+Audio is added to a byte-frozen vision-language base through modality-gated deep
+
+adapters, so gaining audio never changes a single existing text, image, or video
+
+vector. 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) | Technical report: [arXiv:2607.18666](https://arxiv.org/abs/2607.18666)
+
+
+
+## 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 on 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
+
+its benchmark scores, carry over exactly. Audio is added by training 60.6M parameters
+
+(about 2.3% of the stack): a perceiver-resampler that maps frozen
+
+[Qwen2.5-Omni](https://huggingface.co/Qwen/Qwen2.5-Omni-7B) audio-tower features into
+
+the base's input space, plus **28 gated adapters** (44.2M) that give the frozen
+
+language model in-layer capacity for audio. The adapters run only while audio is being
+
+encoded; every other forward pass returns the frozen layers' output untouched, so the
+
+invariance is bitwise, not approximate. Adding audio never invalidates an index you
+
+have already built.
+
+
+
+This v0.3 release ships two revisions of the same model. The default is tuned for
+
+audio-text retrieval; a keyword-tuned revision is available for spoken-command use.
+
+
+
+## Versions in this release
+
+
+
+| Revision | Use it for | AudioCaps a2t R@10 | SpeechCommands |
+
+| --- | --- | --- | --- |
+
+| **`v0.3-preview`** (default) | general audio-text retrieval, RAG, clustering | **0.785** | 0.894 |
+
+| `v0.3-kw-preview` | spoken-keyword and command retrieval | 0.749 | **0.929** |
+
+
+
+The default (`v0.3-preview`) is the recommended model for almost all use. The
+
+keyword-tuned revision trades audio-text retrieval quality for higher zero-shot
+
+keyword-spotting accuracy; use it only when spoken-command matching is the priority,
+
+and use the default otherwise.
+
+
+
+## Highlights (v0.3-preview)
+
+
+
+| Feature | Value |
+
+| --- | --- |
+
+| Parameters | ~2.06B frozen base + 640M frozen audio tower; **60.6M trained** |
+
+| Modalities | text, image, video, audio |
+
+| AudioCaps 883 (audio-to-text) | R@1 0.333 · R@5 0.646 · R@10 **0.785** · mAP@10 0.255 |
+
+| AudioCaps 883 (text-to-audio) | R@1 0.297 · R@5 0.653 · R@10 0.782 · mAP@10 0.445 |
+
+| MAEB(audio-only), 19 tasks | mean **0.454**; leads the board on IEMOCAP speaker-gender (0.938) |
+
+| Non-audio preservation | text, image, video outputs bit-for-bit the base model's |
+
+| Embedding dimension | 2048 (Matryoshka: 64, 128, 256, 512, 1024, 1536, 2048) |
+
+| Base model | Qwen/Qwen3-VL-Embedding-2B (byte-frozen) |
+
+| Audio tower | Qwen/Qwen2.5-Omni-7B audio encoder (frozen) |
+
+
+
+## Evaluation
+
+
+
+<details open>
+
+  <summary>AudioCaps retrieval (883-clip test, min-rank over references)</summary>
+
+
+
+| Model | a2t R@10 | t2a R@10 |
+
+| --- | --- | --- |
+
+| fusion-embedding-2 v0.2 | 0.743 | 0.775 |
+
+| **v0.3-preview** | **0.785** | 0.782 |
+
+| v0.3-kw-preview | 0.749 | 0.771 |
+
+
+
+The v0.3 default improves audio-to-text retrieval by 4.2 points over v0.2.
+
+
+
+</details>
+
+
+
+<details>
+
+  <summary>MAEB(audio-only), full 19-task board — v0.3-preview</summary>
+
+
+
+Mean 0.4536. Scores are the main metric per task.
+
+
+
+| Task | Score |
+
+| --- | --- |
+
+| IEMOCAPGender | 0.938 |
+
+| BeijingOpera | 0.924 |
+
+| NMSQAPairClassification | 0.794 |
+
+| GTZANAudioReranking | 0.717 |
+
+| JamAltArtistA2ARetrieval | 0.687 |
+
+| GTZANGenre | 0.644 |
+
+| VoxPopuliLanguageID | 0.616 |
+
+| MInDS14 | 0.578 |
+
+| CREMADPairClassification | 0.543 |
+
+| MridinghamTonic | 0.540 |
+
+| VoxPopuliAccentPairClassification | 0.509 |
+
+| CREMA_D | 0.277 |
+
+| VoxCelebSA | 0.273 |
+
+| BirdCLEF | 0.184 |
+
+| CommonLanguageAgeDetection | 0.171 |
+
+| SIBFLEURS | 0.115 |
+
+| VoxPopuliGenderClustering | 0.079 |
+
+| VehicleSoundClustering | 0.025 |
+
+| CREMA_DClustering | 0.006 |
+
+
+
+IEMOCAP speaker-gender classification is the strongest cell relative to the field.
+
+Clustering tasks are the weakest and are a target for the next release.
+
+
+
+</details>
+
+
+
+<details>
+
+  <summary>Non-audio preservation and other modalities</summary>
+
+
+
+Text, image, and video outputs are bit-for-bit identical to the frozen
+
+Qwen3-VL-Embedding-2B base (the adapter gate is closed on those inputs). Their
+
+benchmark scores are therefore the base model's own, unchanged by anything added for
+
+audio. Audio-to-image retrieval is emergent: the model is trained on audio-text pairs
+
+only, with no audio-image supervision.
+
+
+
+</details>
+
+
+
+## Notes and limitations
+
+
+
+- **Emotion recognition regressed** relative to v0.2. On RAVDESS zero-shot emotion the
+
+  score is 0.21 (v0.2: 0.35). The v0.3 training mix improved spoken-content
+
+  understanding at the cost of vocal-prosody sensitivity. If speech-emotion is central
+
+  to your use, evaluate before adopting.
+
+- One evaluation cell, `CommonLanguageAgeDetection`, derives from Common Voice, which is
+
+  part of the training data. It scores low (0.171) and does not inflate the reported
+
+  mean, but the potential overlap is noted for completeness.
+
+- These are research previews under CC-BY-NC-4.0.
+
+
+
+## Usage
+
+
+
+<details>
+
+  <summary>Requirements</summary>
+
+
+
+- `fusion_embedding` package: `pip install git+https://github.com/Eximius-Labs/fusion-embedding`
+
+- `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="v0.3-preview",           # default, retrieval-tuned
+
+    # revision="v0.3-kw-preview",      # keyword-tuned alternative
+
+)
+
+
+
+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.
+
+All inputs use the base model's chat-template format; embedding quality is sensitive to
+
+this formatting, so use the templates provided by `FusionEmbedder`.
+
+
+
+</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:
+
+
+
+```python
+
+gallery = FusionEmbedder.center(gallery_embeddings)
+
+```
+
+
+
+</details>
+
+
+
+## Changelog
+
+
+
+- **v0.3-preview** (default): retrieval-tuned flagship. AudioCaps a2t R@10 0.785
+
+  (+0.042 over v0.2); MAEB(audio-only) mean 0.454; leads IEMOCAP speaker-gender.
+
+  RAVDESS emotion regressed to 0.21.
+
+- **v0.3-kw-preview**: keyword-tuned revision. SpeechCommands zero-shot 0.929;
+
+  AudioCaps a2t R@10 0.749.
+
+- **v0.2-preview**: AudioCaps 2.0 fine-tune. a2t R@10 0.743.
+
+- **v0.1-preview**: first modality-gated-adapter release.
+
+
+
+## Deploy on RunPod
+
+
+
+[![Deploy on RunPod](https://api.runpod.io/badge/Eximius-Labs/fusion-embedding)](https://www.runpod.io/console/hub/Eximius-Labs/fusion-embedding)
+
+
+
+One-click deploy the endpoint from the
+
+[RunPod Hub](https://www.runpod.io/console/hub/Eximius-Labs/fusion-embedding) (serverless,
+
+scales to zero when idle). Once it is running, call it:
+
+
+
+```bash
+
+curl -s https://api.runpod.ai/v2/<ENDPOINT_ID>/runsync   -H "Authorization: Bearer $RUNPOD_API_KEY"   -H "Content-Type: application/json"   -d '{"input": {"text": "a dog on a beach"}}'
+
+```
+
+
+
+The endpoint embeds all four modalities. Swap `text` for `image`, `video`, or `audio`
+
+(the value is an https URL, a data URI, or base64). Audio needs the audio tower, so set
+
+`FE2_ENABLE_AUDIO=1` on the endpoint and use a 24 GB or larger GPU. Returns 1024-d
+
+embeddings; add `"dim": 512` to truncate.
+
+
+
+## License
+
+
+
+Weights (all revisions): **CC-BY-NC-4.0** (research preview). The code is Apache-2.0.
+
+The frozen base and audio tower retain their original licenses.
+
+
+
+## Citation
+
+
+
+Model:
+
+
+
+```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}
+
+}
+
+```
+
+
+
+Technical report:
+
+
+
+```bibtex
+
+@article{tonmoy2026fusion,
+
+  title   = {Fusion Embedding: A Unified Embedding Space for Text, Image,
+
+             Video, and Audio},
+
+  author  = {Tonmoy, Abdul Basit and Hoque, Kazi Fardinul and
+
+             Arham, Md. Shahrier Islam and Luthra, Arman},
+
+  journal = {arXiv preprint arXiv:2607.18666},
+
+  year    = {2026}
+
+}
+
+```
+