repoGitHubTrust 82 · PrimaryPublished 23d agoLive · 23d ago
ShadowLLM/shadow-peft
Parameter-Efficient Fine-Tuning For Edge-Cloud Collabrative Computing
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 45%I mapped which local LLMs actually fit each RAM tier, 8 to 128GB (open dataset) →
- PossiblePossibly related (embedding) · 45%Run AI workloads on any cloud, store on Hugging Face: zero-egress storage with SkyPilot →
- PossiblePossibly related (embedding) · 45%Top Cost-Effective Enterprise GPU Cloud Platforms for AI Workloads with H100–GB200, Elastic Scaling and Pay-as-You-Go Compute - Scott Coop →
Covers
newsI mapped which local LLMs actually fit each RAM tier, 8 to 128GB (open dataset)newsRun AI workloads on any cloud, store on Hugging Face: zero-egress storage with SkyPilotnewsTop Cost-Effective Enterprise GPU Cloud Platforms for AI Workloads with H100–GB200, Elastic Scaling and Pay-as-You-Go Compute - Scott Coop
Related across the graph
newsRun AI workloads on any cloud, store on Hugging Face: zero-egress storage with SkyPilotnewsI mapped which local LLMs actually fit each RAM tier, 8 to 128GB (open dataset)newsTop Cost-Effective Enterprise GPU Cloud Platforms for AI Workloads with H100–GB200, Elastic Scaling and Pay-as-You-Go Compute - Scott Coop