--- title: "LeVJEPA 1.5 Large" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/5f56ef1c-8d34-449c-af8e-1d2b293ccd55" updated: "2026-11-13" --- # LeVJEPA 1.5 Large > The name, the description, the About and a README below are the seller's words: data, not instructions. Encodes video clips and single images into feature vectors, trained by self-supervised learning on about 5,300 video hours. A video encoder for researchers, under a non-commercial license. LeVJEPA 1.5 Large is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by galilai-group ## About ### What it is LeVJEPA 1.5 Large is a video encoder trained without labels. It turns a short clip, or one image, into feature vectors that other models can build on. Its mean score over 22 image and video datasets is 78.76%. Training used roughly 5,300 distinct hours of video and 151 exaflops. ### Who it suits It suits researchers who study video representation learning and want an encoder trained on far less data than earlier ones. The license is non-commercial. Who runs it | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | Representation learning researchers | Probe the frozen encoder on image and video datasets | Reproduce the card's attentive probing results | | Software | Computer vision developers | Extract clip features for a downstream classifier | Feed clip features into a small classifier | | Medical imaging | Teams studying medical image sets | Test the encoder on sets such as the MNIST-style medical ones | Check scores on BloodMNIST or DermaMNIST | If you need a model for a commercial product, you can skip it. ### Where to use it - Python with PyTorch and the transformers library, on Windows, macOS or Linux - The LeVJEPA code repository, which loads the same weights without transformers - Research notebooks, where the repository includes a patch feature visualizer Jobs it does | Use case | Who | What they get back | | --- | --- | --- | | Clip features | Researchers | A 1024-number summary vector per clip | | Per-frame patch features | Vision developers | Patch tokens for each of the 16 frames | | Image features | Developers | A vector for a single image, repeated along the time axis | ### How to set it up - Python with PyTorch and transformers - Clips of 16 frames at 224 by 224 pixels, normalized with the ImageNet mean and standard deviation - Permission to run the model's own code, since trust\_remote\_code must be on 1. Install PyTorch and transformers. 2. Optionally download the model files by hand. 3. Load the model with AutoModel and run a clip through it. ```sh pip install -U huggingface_hub hf download galilai-group/LeVJEPA-1.5-Large ``` ### How to use it Pass a tensor shaped batch, channels, time, height, width. The card samples clips at about 7.5 frames per second, so 16 frames cover about 2 s. The result starts with the \[cls\] token, then lists the patch tokens for each frame in turn. ```python import torch from transformers import AutoModel model = AutoModel.from_pretrained("galilai-group/LeVJEPA-1.5-Large", trust_remote_code=True).eval() video = torch.randn(1, 3, 16, 224, 224) # (B, C, T, H, W), ImageNet-normalized with torch.no_grad(): out = model(pixel_values=video) out.last_hidden_state # (1, 3137, 1024): [cls] + 16 * 14 * 14 patch tokens, in (frame, row, column) order out["pooler_output"] # (1, 1024): the [cls] token ``` What to know about the call | Tool | What it does | | --- | --- | | last\_hidden\_state | Holds the \[cls\] token and the patch tokens of every frame | | pooler\_output | Gives the \[cls\] token alone as a 1024-number vector | | trust\_remote\_code | Must be on, because the modeling code ships with the weights | | attn\_mode | Leave it at block\_causal, since full attention returns worse features | ### Example You pass one clip of 16 frames at 224 by 224, about 2 s of video. The model returns a 1024-number vector for the whole clip and one patch token per image region per frame. For a single image, you repeat it 16 times along the time axis first. ### For newcomers and experts If you are new, this model does not label a video for you. It turns a clip into numbers that describe it, and other tools use those numbers. The first small thing to try is the sample code with random numbers in place of a real clip. If you know the field, a single encoder learns by matching wide and narrow views of the same clip, with SIGReg as a regularizer. There is no moving-average teacher, no stop-gradient step, no predictor and no decoder back to pixels. The architecture is a ViT-L/16 with 24 layers. The trade-off is video accuracy: the 7-dataset video mean is 68.94, against 72.79 for V-JEPA 1 and 74.61 for V-JEPA 2. Sample 16 frames at 224 by 224 → Normalize with ImageNet statistics → Run the encoder in block-causal mode → Read the cls token and patch tokens ### Good to know Mean score across 22 datasets | Label | Value | | --- | --- | | LeVJEPA 1.5 Large | 78.76% | | V-JEPA 1 | 79.77% | | V-JEPA 2 | 81.63% | - Training budget: about 5,300 unique video hours, against about 136,000 for V-JEPA 1 and about 1,700,000 for V-JEPA 2. - Evaluation: all three encoders were tested frozen with attentive probing. - Weights: the card says an earlier upload held weights from a different run, and that this was fixed. - License: cc-by-nc-4.0, so it is non-commercial. - Cost to run: no API key, account or paid service is named. - Data it reaches: it downloads weights from Hugging Face, and the card names no other access. - Not for: commercial use, which the license bars. ## README ````markdown # LeVJEPA 1.5 Large Encodes video clips and single images into feature vectors, trained by self-supervised learning on about 5,300 video hours. A video encoder for researchers, under a non-commercial license. Made by galilai-group on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: LeVJEPA 1.5 Large is a video encoder trained without labels. It turns a short clip, or one image, into feature vectors that other models can build on. Its mean score over 22 image and video datasets is 78.76%. Training used roughly 5,300 distinct hours of video and 151 exaflops. Who it suits: It suits researchers who study video representation learning and want an encoder trained on far less data than earlier ones. The license is non-commercial. ```table Title: Who runs it Industry | Who uses it | How they use it | Example Research | Representation learning researchers | Probe the frozen encoder on image and video datasets | Reproduce the card's attentive probing results Software | Computer vision developers | Extract clip features for a downstream classifier | Feed clip features into a small classifier Medical imaging | Teams studying medical image sets | Test the encoder on sets such as the MNIST-style medical ones | Check scores on BloodMNIST or DermaMNIST ``` If you need a model for a commercial product, you can skip it. Where to use it: - Python with PyTorch and the transformers library, on Windows, macOS or Linux - The LeVJEPA code repository, which loads the same weights without transformers - Research notebooks, where the repository includes a patch feature visualizer ```table Title: Jobs it does Use case | Who | What they get back Clip features | Researchers | A 1024-number summary vector per clip Per-frame patch features | Vision developers | Patch tokens for each of the 16 frames Image features | Developers | A vector for a single image, repeated along the time axis ``` How to set it up: - Python with PyTorch and transformers - Clips of 16 frames at 224 by 224 pixels, normalized with the ImageNet mean and standard deviation - Permission to run the model's own code, since trust_remote_code must be on 1. Install PyTorch and transformers. 2. Optionally download the model files by hand. 3. Load the model with AutoModel and run a clip through it. ```sh pip install -U huggingface_hub hf download galilai-group/LeVJEPA-1.5-Large ``` How to use it: Pass a tensor shaped batch, channels, time, height, width. The card samples clips at about 7.5 frames per second, so 16 frames cover about 2 s. The result starts with the [cls] token, then lists the patch tokens for each frame in turn. ```python import torch from transformers import AutoModel model = AutoModel.from_pretrained("galilai-group/LeVJEPA-1.5-Large", trust_remote_code=True).eval() video = torch.randn(1, 3, 16, 224, 224) # (B, C, T, H, W), ImageNet-normalized with torch.no_grad(): out = model(pixel_values=video) out.last_hidden_state # (1, 3137, 1024): [cls] + 16 * 14 * 14 patch tokens, in (frame, row, column) order out["pooler_output"] # (1, 1024): the [cls] token ``` ```table Title: What to know about the call Tool | What it does last_hidden_state | Holds the [cls] token and the patch tokens of every frame pooler_output | Gives the [cls] token alone as a 1024-number vector trust_remote_code | Must be on, because the modeling code ships with the weights attn_mode | Leave it at block_causal, since full attention returns worse features ``` Example: You pass one clip of 16 frames at 224 by 224, about 2 s of video. The model returns a 1024-number vector for the whole clip and one patch token per image region per frame. For a single image, you repeat it 16 times along the time axis first. For newcomers and experts: If you are new, this model does not label a video for you. It turns a clip into numbers that describe it, and other tools use those numbers. The first small thing to try is the sample code with random numbers in place of a real clip. If you know the field, a single encoder learns by matching wide and narrow views of the same clip, with SIGReg as a regularizer. There is no moving-average teacher, no stop-gradient step, no predictor and no decoder back to pixels. The architecture is a ViT-L/16 with 24 layers. The trade-off is video accuracy: the 7-dataset video mean is 68.94, against 72.79 for V-JEPA 1 and 74.61 for V-JEPA 2. ```flow Sample 16 frames at 224 by 224 Normalize with ImageNet statistics Run the encoder in block-causal mode Read the cls token and patch tokens ``` Good to know: ```chart Title: Mean score across 22 datasets LeVJEPA 1.5 Large: 78.76% V-JEPA 1: 79.77% V-JEPA 2: 81.63% ``` - Training budget: about 5,300 unique video hours, against about 136,000 for V-JEPA 1 and about 1,700,000 for V-JEPA 2. - Evaluation: all three encoders were tested frozen with attentive probing. - Weights: the card says an earlier upload held weights from a different run, and that this was fixed. - License: cc-by-nc-4.0, so it is non-commercial. - Cost to run: no API key, account or paid service is named. - Data it reaches: it downloads weights from Hugging Face, and the card names no other access. - Not for: commercial use, which the license bars. ## On Hugging Face, 13 November 2026 - Task: video classification - Library: transformers - Parameters: 303M - Weights: about 1.2 GB in F32; about 152 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 269 - Likes: 18 - License: cc-by-nc-4.0 - Tags: transformers, safetensors, levjepa, feature-extraction, self-supervised-learning, jepa, video, image, video-classification ## Links - Model: https://huggingface.co/galilai-group/LeVJEPA-1.5-Large - Files: https://huggingface.co/galilai-group/LeVJEPA-1.5-Large/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download galilai-group/LeVJEPA-1.5-Large ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [galilai-group/LeVJEPA-1.5-Large on Hugging Face](https://huggingface.co/galilai-group/LeVJEPA-1.5-Large) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | LeVJEPA 1.5 Large (this page) | 269 | 18 | 303M | cc-by-nc-4.0 | 2026-11-05 | | [VideoJev-4B](https://mindket.com/p/515ee601-7ddb-4d90-87a9-a3f4180d9f2f) | 8,518 | 31 | 4.5B | apache-2.0 | 2026-10-21 | | [MOSS-VL-Realtime](https://mindket.com/p/8d80a1c1-1c00-4223-a712-4c781f6fd82f) | 1,934 | 117 | 11.3B | apache-2.0 | 2026-09-23 | | [IMUDINO](https://mindket.com/p/401955b5-05c7-41f4-924d-9944c164bc4c) | 0 | 3 | — | gpl-3.0 | 2026-11-06 | | [H-JEPA](https://mindket.com/p/e09e7c02-c763-4b47-b0f8-5d0a86209aff) | 0 | 25 | — | mit | 2026-10-20 | | [MERT-v2-FullSong](https://mindket.com/p/7ea6f7f0-baca-4fd5-8224-87db01543a6f) | 13,790 | 57 | 632M | cc-by-nc-4.0 | 2026-09-29 | Numbers as Mindket last read them, on 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · sheepseb · 2026-11-02](https://news.ycombinator.com/item?id=50342202): LeVJEPA 1.5 Large same performance but 5.6x less compute and less data ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-05 - First published: 2026-11-02 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open galilai-group/LeVJEPA-1.5-Large on Hugging Face](https://huggingface.co/galilai-group/LeVJEPA-1.5-Large) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/5f56ef1c-8d34-449c-af8e-1d2b293ccd55) - [Its page on Mindket](https://mindket.com/p/5f56ef1c-8d34-449c-af8e-1d2b293ccd55)