DeepLink
Official@deeplink-org
Offers specialized optimization and precision alignment for domestic hardware backends including Ascend, CAMB, and MACA architectures.
Agent Skills by DeepLink
Showing 3 vetted skills indexed across 1 GitHub repositories.
DeepLink-orgsupport-new-model
Guide new LLM or VLM adaptation to dlinfer on domestic hardware.
DeepLink-orgprecision-align
Diagnose precision mismatches between lmdeploy+dlinfer and reference implementations on Ascend, CAMB, and MACA backends.
DeepLink-orggraph-mode-internals
Explain graph mode capture and replay workflows across Ascend, Camb, MACA, and PPU backends.
Frequently Asked Questions About DeepLink
FAQPage SchemaWhat hardware backends are supported for model adaptation?▼
DeepLink supports model adaptation and precision alignment specifically for Ascend, CAMB, MACA, and PPU hardware backends. These capabilities ensure that models function correctly when deployed on domestic compute infrastructure by addressing backend-specific execution requirements.
How can I resolve precision mismatches in my model deployment?▼
You can resolve precision mismatches by utilizing the precision-align capability to diagnose discrepancies between lmdeploy, dlinfer, and reference implementations. This process identifies numerical errors occurring during execution on Ascend, CAMB, or MACA backends to ensure output parity.
What is the primary focus of the graph-mode-internals skill?▼
The graph-mode-internals skill focuses on explaining the capture and replay mechanisms required for efficient execution on Ascend, Camb, MACA, and PPU backends. It provides technical insight into how computational graphs are managed across these specific hardware environments.