Initial upload: gelato-local-nano (frozen encoders + trained projectors)

README.md

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+---
+license: cc-by-nc-4.0
+tags:
+  - multimodal
+  - embeddings
+  - text-to-image
+  - image-text-matching
+  - audio
+  - video
+  - retrieval
+  - gelato
+  - jina-embeddings
+library_name: pytorch
+---
+
+# gelato-local-nano
+
+A local, from-scratch-trained reproduction of **GELATO** (Geometry-preserving
+Embeddings via Locked Aligned TOwers), the architecture behind
+[jina-embeddings-v5-omni](https://arxiv.org/abs/2605.08384). It maps text,
+images, audio, and video into one shared 768-dimensional embedding space,
+trained end-to-end on a single 8GB consumer GPU (RTX 4060 laptop).
+
+This is not a copy of Jina's released checkpoint — the three encoders below
+are frozen, publicly available source models, and only two small projector
+heads (a few million parameters total) were trained from scratch on public
+datasets to align them into a shared space.
+
+## Architecture
+
+Three frozen encoders, two trained projectors — everything large stays
+untouched; only the projectors and two small delimiter vectors have
+gradients:
+
+| Role | Model (frozen) | Params | Role in this repo |
+|---|---|---|---|
+| Text (anchor) | [`jinaai/jina-embeddings-v5-text-nano`](https://huggingface.co/jinaai/jina-embeddings-v5-text-nano) | 239M | Defines the target embedding space; never modified |
+| Vision | [`google/siglip2-base-patch16-naflex`](https://huggingface.co/google/siglip2-base-patch16-naflex) | ~93M | Patch features, variable resolution (NaFlex) |
+| Audio | [`openai/whisper-large-v3`](https://huggingface.co/openai/whisper-large-v3) (encoder only) | ~635M | Frame-level audio features |
+| **Vision projector** (trained) | `checkpoints/vision_projector.pt` | 2.36M | LayerNorm → real 2×2 spatial patch merge → linear (3072→768) |
+| **Audio projector** (trained) | `checkpoints/audio_projector.pt` | 0.98M | Linear (1280→768) |
+
+**Video has no dedicated encoder or projector.** A video is 4 sampled frames
+run through the vision encoder + vision projector, then mean-pooled over
+time — exactly how GELATO's own paper handles video. `checkpoints/video_projector.pt`
+is the vision projector after additional fine-tuning on real video frames
+(see caveat below).
+
+Why these particular substitutions: GELATO's paper describes its vision and
+audio towers as *adapted from* SigLIP2 and Whisper-large-v3, but doesn't
+release those adapted checkpoints publicly — so this reproduction uses the
+public source models directly. The text encoder is an exact match; Jina
+describes v5-omni's text tower as bit-identical to the standalone
+`jina-embeddings-v5-text` release.
+
+## Results (measured on this hardware, not estimated)
+
+Cross-modal retrieval recall@k, text-query direction, against a held-out
+split (higher is better):
+
+| Modality | R@1 | R@5 | R@10 | n |
+|---|---|---|---|---|
+| Image | 13.7% | 68.6% | 81.1% | 1024 |
+| Audio | 67% | 97% | 100% | 33 |
+| Video (held-out) | 40% | 86% | 94% | 50 |
+
+Random-chance R@1 on the 1024-candidate image eval is ~0.1%; all three
+modalities land far above chance. Peak VRAM across every training run
+stayed under 2.7GB — well inside an 8GB budget.
+
+These numbers come after seven rounds of iterating on the vision projector
+specifically (architecture fixes, data scale, data diversity, projector
+capacity) — full write-up of what did and didn't move the needle is in this
+project's [feasibility write-up and README](https://github.com/) *(local
+project — see the blog post that accompanies this model for the full
+round-by-round story)*. Short version: architecture wasn't the bottleneck
+after round 3; raw training data volume was — R@1 only moved once the
+image-caption training set was scaled to ~172k pairs (round 7).
+
+## Usage
+
+```bash
+pip install -r requirements.txt
+```
+
+```python
+from gelato_local import GelatoLocal
+from PIL import Image
+
+model = GelatoLocal.from_pretrained("checkpoints")  # auto-detects cuda/cpu
+
+text_embeds = model.embed_text(["a dog running on the beach"])
+image_embeds = model.embed_image([Image.open("photo.jpg").convert("RGB")])
+
+similarity = text_embeds @ image_embeds.T  # already L2-normalized -> cosine similarity
+```
+
+See `examples/inference_example.py` for a complete runnable script, including
+audio and video usage.
+
+## Important caveat: two different vision checkpoints
+
+`checkpoints/vision_projector.pt` (best pure-image retrieval, the round-7
+result above) and `checkpoints/video_projector.pt` (best video retrieval)
+are **not the same weights**. The video checkpoint was produced by an
+earlier training path that continued a *pre-round-3* vision projector
+(before this project's spatial-merge architecture fix) on real MSR-VTT
+video frames. It was never re-trained against the improved architecture or
+the larger round-7 dataset. Practically: use `vision_projector.pt` for
+image retrieval, `video_projector.pt` for video retrieval, and don't expect
+them to be interchangeable or to represent the same underlying model
+version.
+
+## Scope-downs vs. the GELATO paper
+
+- Training data: ~172k image-caption pairs (Flickr8k + COCO + Conceptual
+  Captions), ~600 audio-caption pairs (AudioCaps), ~180 video-frame pairs
+  (MSR-VTT) — vs. the paper's enterprise-scale, multi-domain corpus.
+- Batch size up to 128 vs. the paper's 256.
+- No task-specific LoRA adapters (retrieval/classification/clustering)
+  layered on top — base cross-modal alignment only.
+- Six rounds of experimentation ruled out architecture (merge fidelity,
+  projector capacity) as the remaining gap to the paper's published numbers;
+  data volume was the one lever that reliably helped, and this repo's
+  training data is still 2-3 orders of magnitude smaller than an
+  enterprise-scale corpus.
+
+## License
+
+This repo is licensed **CC-BY-NC-4.0** (non-commercial), matching the most
+restrictive license among its components:
+[`jina-embeddings-v5-text-nano`](https://huggingface.co/jinaai/jina-embeddings-v5-text-nano)
+is CC-BY-NC-4.0. `siglip2-base-patch16-naflex` and `whisper-large-v3` are
+both Apache-2.0. If you plan to use this for anything commercial, you'll
+need a compatible license for the text encoder specifically — check with
+Jina AI directly.
+
+## Citation
+
+This repo reproduces the architecture described in:
+
+```
+@article{gelato2026,
+  title={jina-embeddings-v5-omni / GELATO},
+  note={arXiv:2605.08384}
+}
+```
+
+Not affiliated with or endorsed by Jina AI — an independent, from-scratch
+reproduction built for a learning/portfolio project.

checkpoints/audio_projector.pt

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+size 3940356

checkpoints/video_projector.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d85127853776872aed34f69d7ad5d13ddac3abf6f0f95cdb069e94d9350a07b8
+size 9445380

checkpoints/vision_projector.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f486aeef4fefd959a9e567d39e3b290460242c4efbf09569233d4cffa8b5bdaf
+size 9445394

examples/inference_example.py

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+"""Embed text, an image, and a video into the same 768-dim space and compare
+with cosine similarity (embeddings are already L2-normalized, so a plain dot
+product is cosine similarity).
+
+Run from the repo root:
+    pip install -r requirements.txt
+    python examples/inference_example.py
+"""
+from PIL import Image
+
+from gelato_local import GelatoLocal
+
+model = GelatoLocal.from_pretrained("checkpoints")  # device auto-detects cuda/cpu
+
+texts = ["a photo of a dog running on the beach", "a bowl of ramen noodles"]
+text_embeds = model.embed_text(texts)
+
+image = Image.open("your_image.jpg").convert("RGB")  # replace with a real path
+image_embeds = model.embed_image([image])
+
+similarity = text_embeds @ image_embeds.T
+for text, score in zip(texts, similarity[:, 0].tolist()):
+    print(f"{score:.3f}  {text}")
+
+# video_embeds = model.embed_video_file("your_clip.mp4")  # uses the video-tuned projector, see README
+# audio_embeds = model.embed_audio([your_16khz_float32_array])

gelato_local/__init__.py

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+"""gelato-local-nano: a local, from-scratch-trained reproduction of GELATO
+(the architecture behind jina-embeddings-v5-omni) -- frozen text/vision/audio
+encoders plus two small trained projectors, sharing one 768-dim embedding
+space across text, image, audio, and video.
+
+    from gelato_local import GelatoLocal
+
+    model = GelatoLocal.from_pretrained("checkpoints", device="cuda")
+    text_embeds = model.embed_text(["a dog running on the beach"])
+    image_embeds = model.embed_image([Image.open("photo.jpg")])
+    similarity = text_embeds @ image_embeds.T
+"""
+import os
+
+import torch
+import torch.nn.functional as F
+
+from .encoders import AudioEncoder, TextEncoder, VisionEncoder
+from .projectors import AudioProjector, VisionProjector
+from .video import embed_video, sample_frames
+
+__all__ = [
+    "GelatoLocal",
+    "TextEncoder",
+    "VisionEncoder",
+    "AudioEncoder",
+    "VisionProjector",
+    "AudioProjector",
+    "embed_video",
+    "sample_frames",
+]
+
+
+class GelatoLocal:
+    """Loads all three frozen encoders plus the trained projectors, and
+    exposes one `embed_*` method per modality, each returning an L2-normalized
+    [batch, 768] tensor in the shared space -- so any two outputs from any
+    two modalities can be compared with a plain dot product / cosine
+    similarity."""
+
+    def __init__(self, device: str = "cuda" if torch.cuda.is_available() else "cpu"):
+        self.device = device
+        self.text_encoder = TextEncoder(device)
+        self.vision_encoder = VisionEncoder(device)
+        self.audio_encoder = AudioEncoder(device)
+        self.vision_projector = VisionProjector(patch_dim=768, out_dim=768).to(device).eval()
+        self.audio_projector = AudioProjector(in_dim=1280, out_dim=768).to(device).eval()
+        self.video_projector = VisionProjector(patch_dim=768, out_dim=768).to(device).eval()
+
+    @classmethod
+    def from_pretrained(cls, checkpoint_dir: str, device: str = "cuda" if torch.cuda.is_available() else "cpu"):
+        model = cls(device=device)
+        model.vision_projector.load_state_dict(
+            torch.load(os.path.join(checkpoint_dir, "vision_projector.pt"), map_location=device)
+        )
+        model.audio_projector.load_state_dict(
+            torch.load(os.path.join(checkpoint_dir, "audio_projector.pt"), map_location=device)
+        )
+        model.video_projector.load_state_dict(
+            torch.load(os.path.join(checkpoint_dir, "video_projector.pt"), map_location=device)
+        )
+        return model
+
+    @torch.no_grad()
+    def embed_text(self, texts: list[str]) -> torch.Tensor:
+        return F.normalize(self.text_encoder.embed(texts).float(), dim=-1)
+
+    @torch.no_grad()
+    def embed_image(self, images: list) -> torch.Tensor:
+        patch_tokens, mask, spatial_shapes = self.vision_encoder.patch_tokens(images)
+        out = self.vision_projector(patch_tokens.float(), mask, spatial_shapes)
+        return F.normalize(out, dim=-1)
+
+    @torch.no_grad()
+    def embed_audio(self, arrays: list, sampling_rate: int = 16000) -> torch.Tensor:
+        frame_tokens = self.audio_encoder.frame_tokens(arrays, sampling_rate=sampling_rate)
+        out = self.audio_projector(frame_tokens.float())
+        return F.normalize(out, dim=-1)
+
+    @torch.no_grad()
+    def embed_video_file(self, video_path: str, num_frames: int = 4) -> torch.Tensor:
+        out = embed_video(self.vision_encoder, self.video_projector, video_path, self.device, num_frames=num_frames)
+        return F.normalize(out.unsqueeze(0), dim=-1)

gelato_local/encoders.py

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+"""Frozen encoder loaders.
+
+Substitutions vs. the GELATO paper's exact checkpoints, since the paper's
+adapted encoders (Qwen3.5's vision tower, Qwen2.5-Omni's audio tower) aren't
+distributed as standalone checkpoints, and the paper itself says they were
+*adapted from* these two source models -- so we use the source models
+directly, which the paper's own dimension numbers (1280 audio, patch-merge
+producing 3072 for nano) line up with:
+  text  : jinaai/jina-embeddings-v5-text-nano (exact match, publicly released)
+  vision: google/siglip2-base-patch16-naflex   (source SigLIP2 GELATO adapted)
+  audio : openai/whisper-large-v3 encoder      (source Whisper GELATO adapted)
+"""
+import torch
+import torch.nn as nn
+from transformers import (
+    AutoModel,
+    AutoTokenizer,
+    AutoImageProcessor,
+    WhisperModel,
+    WhisperFeatureExtractor,
+)
+
+try:
+    from transformers import Siglip2VisionModel as _VisionModelClass
+except ImportError:
+    from transformers import AutoModel as _VisionModelClass
+
+TEXT_MODEL_ID = "jinaai/jina-embeddings-v5-text-nano"
+VISION_MODEL_ID = "google/siglip2-base-patch16-naflex"
+AUDIO_MODEL_ID = "openai/whisper-large-v3"
+
+
+def _freeze(module: nn.Module) -> nn.Module:
+    module.eval()
+    for p in module.parameters():
+        p.requires_grad_(False)
+    return module
+
+
+def mean_pool(last_hidden_state: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor:
+    mask = attention_mask.unsqueeze(-1).to(last_hidden_state.dtype)
+    summed = (last_hidden_state * mask).sum(dim=1)
+    counts = mask.sum(dim=1).clamp(min=1e-9)
+    return summed / counts
+
+
+class TextEncoder:
+    def __init__(self, device: str, dtype=torch.bfloat16, max_length: int = 512):
+        self.tokenizer = AutoTokenizer.from_pretrained(TEXT_MODEL_ID, trust_remote_code=True)
+        self.model = _freeze(
+            AutoModel.from_pretrained(TEXT_MODEL_ID, trust_remote_code=True, dtype=dtype).to(device)
+        )
+        self.device = device
+        self.max_length = max_length
+
+    @torch.no_grad()
+    def embed(self, texts: list[str]) -> torch.Tensor:
+        inputs = self.tokenizer(
+            texts, padding=True, truncation=True, max_length=self.max_length, return_tensors="pt"
+        ).to(self.device)
+        out = self.model(**inputs)
+        return mean_pool(out.last_hidden_state, inputs["attention_mask"])
+
+
+class VisionEncoder:
+    def __init__(self, device: str, dtype=torch.bfloat16):
+        self.processor = AutoImageProcessor.from_pretrained(VISION_MODEL_ID)
+        self.model = _freeze(_VisionModelClass.from_pretrained(VISION_MODEL_ID, dtype=dtype).to(device))
+        self.device = device
+
+    @torch.no_grad()
+    def patch_tokens(self, images: list) -> tuple[torch.Tensor, torch.Tensor | None, torch.Tensor | None]:
+        """Returns (patch_tokens [B,N,D], attention_mask [B,N] or None,
+        spatial_shapes [B,2] or None -- per-image (H_patches, W_patches)
+        before NaFlex pads every image in the batch to the same N, needed
+        for a real spatial 2x2 merge instead of grouping arbitrary tokens."""
+        inputs = self.processor(images=images, return_tensors="pt").to(self.device)
+        vision_module = getattr(self.model, "vision_model", self.model)
+        out = vision_module(**inputs)
+        mask = inputs.get("pixel_attention_mask")
+        spatial_shapes = inputs.get("spatial_shapes")
+        return out.last_hidden_state, mask, spatial_shapes
+
+
+class AudioEncoder:
+    def __init__(self, device: str, dtype=torch.bfloat16):
+        self.feature_extractor = WhisperFeatureExtractor.from_pretrained(AUDIO_MODEL_ID)
+        full = WhisperModel.from_pretrained(AUDIO_MODEL_ID, dtype=dtype)
+        self.model = full.encoder
+        del full.decoder
+        del full
+        self.model = _freeze(self.model.to(device))
+        self.device = device
+        self.dtype = dtype
+
+    @torch.no_grad()
+    def frame_tokens(self, audio_arrays: list, sampling_rate: int = 16000) -> torch.Tensor:
+        inputs = self.feature_extractor(
+            audio_arrays, sampling_rate=sampling_rate, return_tensors="pt"
+        ).to(self.device)
+        features = inputs["input_features"].to(self.dtype)
+        out = self.model(features)
+        return out.last_hidden_state

gelato_local/projectors.py

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+"""Trainable projectors that map frozen vision/audio encoder outputs into the
+frozen text encoder's embedding space -- the only parameters GELATO trains.
+
+Everything upstream (SigLIP2, Whisper encoder, jina-v5-text-nano) stays frozen;
+only VisionProjector.fc_vision_2 and AudioProjector.fc_audio get gradients.
+"""
+import torch
+import torch.nn as nn
+
+
+class VisionProjector(nn.Module):
+    """LayerNorm -> real 2x2 spatial merge -> trainable linear, matching
+    GELATO's "LayerNorm, 2x2 spatial merge, fc_vision_2" description
+    (nano: 3072->768).
+
+    Uses NaFlex's per-image `spatial_shapes` (H_patches, W_patches) to group
+    each image's own patch grid into genuine 2x2 spatial blocks before
+    concatenating channel-wise -- earlier versions approximated this with
+    sequential 4-token grouping (mixing patches from unrelated rows), which
+    is architecturally wrong and is kept here only as a fallback for
+    callers that don't have spatial_shapes available.
+    """
+
+    def __init__(self, patch_dim: int, out_dim: int, merge: int = 4, hidden_dim: int | None = None):
+        super().__init__()
+        self.merge = merge
+        self.norm = nn.LayerNorm(patch_dim, elementwise_affine=False)
+        if hidden_dim:
+            # not part of GELATO's own recipe (which uses a single linear
+            # fc_vision_2) -- an experiment to test whether a linear
+            # projector's capacity, not data, is capping R@1. See README's
+            # "round 6" for the result.
+            self.fc_vision_2 = nn.Sequential(
+                nn.Linear(patch_dim * merge, hidden_dim), nn.GELU(), nn.Linear(hidden_dim, out_dim)
+            )
+        else:
+            self.fc_vision_2 = nn.Linear(patch_dim * merge, out_dim)
+        self.modality_delim = nn.Parameter(torch.zeros(out_dim))
+
+    def _spatial_merge_one(self, tokens: torch.Tensor, h: int, w: int) -> torch.Tensor:
+        # tokens: [N, D] for one image, first h*w entries are the valid grid
+        d = tokens.shape[-1]
+        h2, w2 = h - (h % 2), w - (w % 2)  # drop a trailing odd row/col rather than fabricate one
+        grid = tokens[: h * w].view(h, w, d)[:h2, :w2]
+        blocks = grid.reshape(h2 // 2, 2, w2 // 2, 2, d).permute(0, 2, 1, 3, 4).reshape(-1, 4 * d)
+        return blocks
+
+    def forward(
+        self,
+        patch_tokens: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        spatial_shapes: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        # patch_tokens: [B, N, patch_dim]
+        b, n, d = patch_tokens.shape
+        patch_tokens = self.norm(patch_tokens)
+
+        if spatial_shapes is not None:
+            pooled = []
+            for i in range(b):
+                h, w = int(spatial_shapes[i, 0]), int(spatial_shapes[i, 1])
+                blocks = self._spatial_merge_one(patch_tokens[i], h, w)
+                projected = self.fc_vision_2(blocks)  # [num_blocks, out_dim]
+                pooled.append(projected.mean(dim=0))
+            pooled = torch.stack(pooled, dim=0)
+        else:
+            pad = (-n) % self.merge
+            pt = patch_tokens
+            if pad:
+                pt = nn.functional.pad(pt, (0, 0, 0, pad))
+                if attention_mask is not None:
+                    attention_mask = nn.functional.pad(attention_mask, (0, pad))
+            merged = pt.reshape(b, pt.shape[1] // self.merge, d * self.merge)
+            projected = self.fc_vision_2(merged)  # [B, N/merge, out_dim]
+            if attention_mask is not None:
+                merged_mask = attention_mask.reshape(b, -1, self.merge).amax(dim=-1).to(projected.dtype)
+                weights = merged_mask.unsqueeze(-1)
+                pooled = (projected * weights).sum(dim=1) / weights.sum(dim=1).clamp(min=1e-6)
+            else:
+                pooled = projected.mean(dim=1)
+
+        return pooled + self.modality_delim
+
+
+class AudioProjector(nn.Module):
+    """Trainable linear (1280->768 for nano), matching GELATO's fc_audio."""
+
+    def __init__(self, in_dim: int, out_dim: int):
+        super().__init__()
+        self.fc_audio = nn.Linear(in_dim, out_dim)
+        self.modality_delim = nn.Parameter(torch.zeros(out_dim))
+
+    def forward(self, frame_tokens: torch.Tensor, attention_mask: torch.Tensor | None = None) -> torch.Tensor:
+        # frame_tokens: [B, T, in_dim]
+        projected = self.fc_audio(frame_tokens)
+        if attention_mask is not None:
+            weights = attention_mask.unsqueeze(-1).to(projected.dtype)
+            pooled = (projected * weights).sum(dim=1) / weights.sum(dim=1).clamp(min=1e-6)
+        else:
+            pooled = projected.mean(dim=1)
+        return pooled + self.modality_delim
+
+
+def embed_video_from_frames(vision_projector: VisionProjector, per_frame_patch_tokens: list[torch.Tensor]) -> torch.Tensor:
+    """Video = mean of per-frame vision-projector embeddings.
+
+    No dedicated video encoder, matching GELATO: sample frames, run each
+    through the (shared, already-trained) vision projector, pool over time.
+    """
+    frame_embeds = torch.stack([vision_projector(tokens) for tokens in per_frame_patch_tokens], dim=1)  # [B, F, out_dim]
+    return frame_embeds.mean(dim=1)

gelato_local/video.py

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+"""Frame sampling + video embedding. No video encoder exists here -- a video
+is just N sampled frames run through the already-trained VisionProjector and
+mean-pooled, exactly as GELATO does."""
+import cv2
+import numpy as np
+import torch
+from PIL import Image
+
+
+def sample_frames(video_path: str, num_frames: int = 4) -> list:
+    cap = cv2.VideoCapture(video_path)
+    total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    if total <= 0:
+        cap.release()
+        raise ValueError(f"no frames read from {video_path}")
+    indices = np.linspace(0, total - 1, num=min(num_frames, total), dtype=int)
+    frames = []
+    for idx in indices:
+        cap.set(cv2.CAP_PROP_POS_FRAMES, int(idx))
+        ok, frame_bgr = cap.read()
+        if not ok:
+            continue
+        frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB)
+        frames.append(Image.fromarray(frame_rgb))
+    cap.release()
+    if not frames:
+        raise ValueError(f"no frames decoded from {video_path}")
+    return frames
+
+
+@torch.no_grad()
+def embed_video(vision_encoder, vision_projector, video_path: str, device: str, num_frames: int = 4) -> torch.Tensor:
+    frames = sample_frames(video_path, num_frames=num_frames)
+    patch_tokens, mask, spatial_shapes = vision_encoder.patch_tokens(frames)  # [num_frames, N, 768]
+    frame_embeds = vision_projector(patch_tokens.float(), mask, spatial_shapes)  # [num_frames, 768]
+    return frame_embeds.mean(dim=0)

requirements.txt

@@ -0,0 +1,7 @@
+torch>=2.1
+transformers>=4.45
+pillow
+opencv-python
+soundfile
+librosa
+numpy