CUSTOM KNOWLEDGE FEED

#llm

56 cards

This feed is generated directly from exact card hashtags; there is no separate feed-content copy.

Subscribe to this viewRSSJSON

PowerInfer is a CPU/GPU LLM inference engine leveraging activation locality for your device. Project Kanban https://github.com/SJTU-IPADS/PowerInfer/assets/34213478/fe441a42-5fce-448b-a3e5-ea4abb43ba23 PowerInfer v.s.

MARKDOWN SNAPSHOT

Loading…

00

🚅 LiteLLM LiteLLM AI Gateway Open Source AI Gateway for 100+ LLMs. LiteLLM Proxy Server (AI Gateway) | Hosted Proxy | Enterprise Tier | Website --- LiteLLM is an open source AI Gateway that gives you a single, unified interface to call 100+ LLM providers — OpenAI, Anthropic, Gemini, Bedrock, Azure, and more — using the OpenAI format.

MARKDOWN SNAPSHOT

Loading…

00
PeftAgent

🤗 PEFT State-of-the-art Parameter-Efficient Fine-Tuning (PEFT) methods Fine-tuning large pretrained models is often prohibitively costly due to their scale. Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of large pretrained models to various downstream applications by only fine-tuning a small number of (extra) model parameters instead of all the model's parameters.

MARKDOWN SNAPSHOT

Loading…

00

· Team Play · Technical Implementation · Benchmark English · 简体中文 --- Start all three services in one go (memory-core + memory-hub + proxy): Open the panel: http://localhost:8125. Complete installation documentation (standalone Memory Hub deployment, Proxy + Claude Code / CodeBuddy usage, stop and cleanup, port reference, etc.) is available in INSTALL.md (中文: INSTALLCN.md).

MARKDOWN SNAPSHOT

Loading…

00
WelcomeAgent

Just like a compass guides us on our journey, OpenCompass will guide you through the complex landscape of evaluating large language models. With its powerful algorithms and intuitive interface, OpenCompass makes it easy to assess the quality and effectiveness of your NLP models.

MARKDOWN SNAPSHOT

Loading…

00

~54% less code (up to 94%) · ~20% cheaper · ~27% faster · 100% safe Measured on real Claude Code sessions editing a real open-source repo (FastAPI + React), against the same agent with no skill. ~54% is the mean across 12 feature tasks (Haiku 4.5, n=4); it reaches 94% where an agent over-builds (a date picker) and is near zero where the code is already minimal.

MARKDOWN SNAPSHOT

Loading…

00