Results for “kv-cache”
17 skills
lingxling Hf Mem
Estimates memory requirements for running Hugging Face models, including optional KV cache, using HTTP range requests without downloading weights.
253
muratcankoylan Latent Briefing
Shares memory between agents at the representation level by compacting the orchestrator's KV cache for efficient worker handoff, reducing token costs without summarization or retrieval.
16.9k · bundle
nvidia Dynamo Router Starter
Start or patch Dynamo router modes and run router endpoint smoke checks for round-robin, KV-aware, least-loaded, or device-aware routing.
2.2k · bundle
jrennie99-glitch Turboquant
KV cache compression for LLM inference — 4.4x compression, 2x context capacity, near-lossless quality. ICLR 2026 paper implementation with vLLM integration.
0
muratcankoylan Context Optimization
Extends effective context capacity through strategic compression, masking, caching, and partitioning techniques.
16.9k · bundle
More results
huggingface Hf Mem
Estimates the memory required to load Safetensors or GGUF model weights for inference from the Hugging Face Hub, using HTTP Range requests without downloading weights.
10.8k
antigravity Hf Mem
Estimates GPU memory required to load Safetensors or GGUF model weights for inference from the Hugging Face Hub using HTTP Range requests, without downloading weights locally.
42.4k
a5c-ai Kip Librarian
Canonical kip knowledge-store patterns for any process or agent: recall prior facts before work, assert decisions/gate outcomes/rejections as structured facts after work, resolve entities with explicit --model sonnet, and invoke the CLI Windows-safely via node packages/kip-sdk/dist/cli/kip.js when kip is not on PATH.
1.7k · bundle
manu14357 QA
Interactive QA session where user reports bugs or issues conversationally, and the agent files GitHub issues. Explores the codebase in the background for context and domain language. Use when user wants to report bugs, do QA, file issues conversationally, or mentions "QA session".
16
azusagasaku Knowledge Ops
跨多个存储层（本地文件、MCP memory、向量存储、Git 仓库）的知识库管理、摄取、同步和检索。在用户想要保存、组织、同步、去重或跨知识系统搜索时使用。
0
machenjie Cache Design
Use with task-agent or review-agent for task-local cache scope, freshness, invalidation, and source-load risk. Do not use without a cache decision or as task owner.
4 · bundle
fradser QA
Interactive QA session where user reports bugs or issues conversationally, and the agent files GitHub issues. Explores the codebase in the background for context and domain language. Use when user wants to report bugs, do QA, file issues conversationally, or mentions "QA session".
580
jiachen-t-wang Coyo 700m Image Text Pair Dataset Github Kakaobrain Coyo 700
COYO-700M: Image-Text Pair Dataset
6
tianhao909 Rwkv Architecture
RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.
1 · bundle
qcmuu Rwkv Architecture
RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.
0 · bundle
tianhao909 Mamba Architecture
State-space model with O(n) complexity vs Transformers' O(n²). 5× faster inference, million-token sequences, no KV cache. Selective SSM with hardware-aware design. Mamba-1 (d_state=16) and Mamba-2 (d_state=128, multi-head). Models 130M-2.8B on HuggingFace.
1 · bundle
qcmuu Mamba Architecture
State-space model with O(n) complexity vs Transformers' O(n²). 5× faster inference, million-token sequences, no KV cache. Selective SSM with hardware-aware design. Mamba-1 (d_state=16) and Mamba-2 (d_state=128, multi-head). Models 130M-2.8B on HuggingFace.
0 · bundle