Model overview
This repository hosts the **clip-vit-base-patch32** models exported for the React Native ExecuTorch library as ExecuTorch `.pte` programs, ready to run on device. The `xnnpack/*_int8.pte` towers are 8da8w (int8 dynamic activations, int8 per-channel weights) on the Linear layers; the text tower also has an int8 token embedding table. ImageNetV2 zero-shot top-1 (1000 images, one per class, "a photo of a {class}."): These files are published for the **ExecuTorch v1.4.1** runtime. ExecuTorch gives no forward compatibility guarantee, so an older runtime may fail to load them.
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：mit。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。