Model overview
A ViT-L/16 video encoder trained with LeV-JEPA, a self-supervised objective combining a multi-crop prediction loss with SIGReg. No labels are used at any point. `trust_remote_code=True` is required: this is a custom architecture (RoPE + block-causal attention) rather than a stock `transformers` model, so the modeling code ships with the weights. Normalise with the ImageNet statistics used in training — `mean=[0.485, 0.456, 0.406]`, `std=[0.229, 0.224, 0.225]` — and resize/crop to 224. Frames are sampled at roughly 7.5 fps in training, so a 16-frame clip covers about two seconds.
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.
| Source / channel | Input | Output | Billing unit | Collected at |
|---|---|---|---|---|
| No verified prices yet. Check the source channel. | ||||
Estimate monthly base token costs
Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.
Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
View full indexed rankings →Frequently asked questions
Do open weights permit free commercial use?
Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.
Are these real-time billing prices?
These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.
Sources & traceability
许可证：cc-by-nc-4.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。