Model reference · open weights
uform3-image-text-multilingual is an open-weight embedding model from unum-cloud. uform3-image-text-multilingual-base (BF16) weighs 412 MB; the smallest configuration that runs it is RTX 3060 12 GB.
For self-hosting, compare this build's requirements below with the GPU you plan to rent.
Summary of the unum-cloud/uform3-image-text-multilingual-base model card, 2026-10-01
What it is
| Released by | unum-cloud |
|---|---|
| Released | 2024-04-20 |
| VRAM | 412 MB for the weights |
What it runs on
| Card | Runs | Memory |
|---|---|---|
| RTX 3060 12 GB | fits | 11.6 GB |
| RTX 4060 Ti 16 GB | fits | 15.4 GB |
| RTX 3090 24 GB | fits | 23.4 GB |
| RTX 4090 24 GB | fits | 23.4 GB |
| RTX 5090 32 GB | fits | 31.0 GB |
| L40S 48 GB | fits | 44.0 GB |
| A100 80 GB | fits | 78.2 GB |
| H100 80 GB | fits | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | fits | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | fits | 107 GB |
| H200 141 GB | fits | 138 GB |
| B200 180 GB | fits | 176 GB |
From the model card
Pocket-Sized Multimodal AI For Content Understanding and Generation In Python, JavaScript, and Swift
The uform3-image-text-multilingual-base UForm model is a tiny vision and multilingual language encoder, covering 21 languages, mapping them into a shared vector space.
This model produces up to 256-dimensional embeddings and is made of:
Unlike most CLIP-like multomodal models, this model shares 4 layers between the text and visual encoder to allow for more data- and parameter-efficient training. Also unlike most models, UForm provides checkpoints compatible with PyTorch, ONNX, and CoreML, covering the absolute majority of AI-capable devices, with pre-quantized weights and inference code. If you need a larger, more accurate, or multilingual model, check our HuggingFace Hub. For more details on running the model, check out the UForm GitHub repository.
For all evaluations, the multimodal part was used unless otherwise stated.
| Dataset | Recall@1 | Recall@5 | Recall@10 |
|---|---|---|---|
| Zero-Shot Flickr | 0.558 | 0.813 | 0.874 |
| MS-COCO ¹ | 0.401 | 0.680 | 0.781 |
¹ It's important to note, that the MS-COCO train split was present in the training data.
Recall@10 on the XTD-10 dataset:
| English | German | Spanish | French | Italian | Russian | Japanese | Korean | Turkish | Chinese | Polish |
|---|---|---|---|---|---|---|---|---|---|---|
| 96.1 | 93.5 | 95.7 | 94.1 | 94.4 | 90.4 | 90.2 | 91.3 | 95.2 | 93.8 | 95.8 |
Recall@1, Recall@5, and Recall@10 on the COCO-SM dataset:
| Target Language | OpenCLIP @ 1 | UForm @ 1 | OpenCLIP @ 5 | UForm @ 5 | OpenCLIP @ 10 | UForm @ 10 | Speakers |
|---|---|---|---|---|---|---|---|
| Arabic | 22.7 | 31.7 | 44.9 | 57.8 | 55.8 | 69.2 | 274 M |
| Armenian | 5.6 | 22.0 | 14.3 | 44.7 | 20.2 | 56.0 | 4 M |
| Chinese | 27.3 | 32.2 | 51.3 | 59.0 | 62.1 | 70.5 | 1'118 M |
| English | 37.8 | 37.7 | 63.5 | 65.0 | 73.5 | 75.9 | 1'452 M |
| French | 31.3 | 35.4 | 56.5 | 62.6 | 67.4 | 73.3 | 274 M |
| German | 31.7 | 35.1 | 56.9 | 62.2 | 67.4 | 73.3 | 134 M |
| Hebrew | 23.7 | 26.7 | 46.3 | 51.8 | 57.0 | 63.5 | 9 M |
| Hindi | 20.7 | 31.3 | 42.5 | 57.9 | 53.7 | 69.6 | 602 M |
| Indonesian | 26.9 | 30.7 | 51.4 | 57.0 | 62.7 | 68.6 | 199 M |
| Italian | 31.3 | 34.9 | 56.7 | 62.1 | 67.1 | 73.1 | 67 M |
| Japanese | 27.4 | 32.6 | 51.5 | 59.2 | 62.6 | 70.6 | 125 M |
| Korean | 24.4 | 31.5 | 48.1 | 57.8 | 59.2 | 69.2 | 81 M |
| Persian | 24.0 | 28.8 | 47.0 | 54.6 | 57.8 | 66.2 | 77 M |
| Polish | 29.2 | 33.6 | 53.9 | 60.1 | 64.7 | 71.3 | 41 M |
| Portuguese | 31.6 | 32.7 | 57.1 | 59.6 | 67.9 | 71.0 | 257 M |
| Russian | 29.9 | 33.9 | 54.8 | 60.9 | 65.8 | 72.0 | 258 M |
| Spanish | 32.6 | 35.6 | 58.0 | 62.8 | 68.8 | 73.7 | 548 M |
| Thai | 21.5 | 28.7 | 43.0 | 54.6 | 53.7 | 66.0 | 61 M |
| Turkish | 25.5 | 33.0 | 49.1 | 59.6 | 60.3 | 70.8 | 88 M |
| Ukranian | 26.0 | 30.6 | 49.9 | 56.7 | 60.9 | 68.1 | 41 M |
| Vietnamese | 25.4 | 28.3 | 49.2 | 53.9 | 60.3 | 65.5 | 85 M |
| Mean | 26.5±6.4 | 31.8±3.5 | 49.8±9.8 | 58.1±4.5 | 60.4±10.6 | 69.4±4.3 | - |
| Google Translate | 27.4±6.3 | 31.5±3.5 | 51.1±9.5 | 57.8±4.4 | 61.7±10.3 | 69.1±4.3 | - |
| Microsoft Translator | 27.2±6.4 | 31.4±3.6 | 50.8±9.8 | 57.7±4.7 | 61.4±10.6 | 68.9±4.6 | - |
| Meta NLLB | 24.9±6.7 | 32.4±3.5 | 47.5±10.3 | 58.9±4.5 | 58.2±11.2 | 70.2±4.3 | - |
For a deeper comparison of output ranking check the following table for the Normalized Discounted Cumulative Gains for the first 20 results - NDCG@20:
| | Arabic | Armenian | Chinese | French | German | Hebrew | Hindi | Indonesian | Italian | Japanese |
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.