Causal-Attention Diffusion LM: How WeDLM Outperforms vLLM Without Custom Kernels
1 months ago 高效码农WeDLM in Practice: How to Deploy a Causal-Attention Diffusion LM That Outruns vLLM Without New Kernels TL;DR: WeDLM keeps causal attention, reorders tokens so masked positions still see all observed context, and commits tokens left-to-right as soon as they are predicted. The result is the first diffusion-style language model that beats a production vLLM baseline in wall-clock time while preserving (and sometimes improving) accuracy. This post explains why it works, how to run it, and what to watch when you ship it. What exact problem does WeDLM solve? Question answered: “Why do most diffusion language models feel fast in papers …
Sim Studio AI Workflow Builder: Build & Host Agent Pipelines in 10 Minutes
1 months ago 高效码农Sim Studio in 10 Minutes: Build, Host, and Run Your Own AI-Agent Pipeline—No Code, Full Control Can I really sketch an AI workflow on a canvas, feed it my own documents, and keep everything offline on my GPU laptop? Yes—Sim Studio ships the same repo in four flavors: cloud, npm one-liner, Docker Compose, and dev container. Pick one, and your first agent is live before coffee finishes dripping. Table of Contents Cloud Route: fastest public preview Self-Hosted Playbook: four rigor levels Knowledge Base in Practice: PDF → vectors → answers Local LLM Options: Ollama vs. vLLM Troubleshooting Field Guide Author’s …
Kimi K2 Tool Calling on vLLM: A Complete Debugging Guide for 4x Success
1 months ago 高效码农Achieving Reliable Tool Calling with Kimi K2 on vLLM: A Comprehensive Debugging Guide If you’ve been working with large language models, you know how exciting agentic workflows can be. The ability for models to call tools reliably opens up possibilities for complex applications, from automated research to advanced coding assistants. Moonshot AI’s Kimi K2 series stands out in this area, with impressive tool calling performance. Naturally, many developers want to run it on high-performance open-source inference engines like vLLM. When I first tried deploying Kimi K2 on vLLM and running the official K2-Vendor-Verifier benchmark, the results were disappointing. The tool …