Running Llama2 Models in Vanilla Minecraft With Pure Commands
I made a program that converts any llama2 large language model into a minecraft datapack, and you can run inference right inside the game. It's still semi-finished, Currently I've only implemented argmax sampling, so the output tends to stuck in loops sometimes. Adding top-p sampling will probably improve this a lot. The tokenizer is also missing for now, it can only generate text from scratch. Inference speed is...quite slow. With a 15M parameter model, it takes roughly 20 minutes to produce a single token. If you want to try it out yourself, you can download "stories15M.bin" and "tokenizer.bin" from llama2.c , and follow the instructions in my repository down below. I will keep working on this project, hopefully one day I will be able to bring a usable chat model in Minecraft. Github Rep
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From Desktop to Web: A Guide to Publishing and Embedding Power BI Reports
Power BI is a powerful business intelligence tool that transforms raw data into immersive, interactive visual stories. However, the true value of a report is realized only when it is shared with stakeholders. Publishing is the process of moving your report from the local Power BI Desktop environment to the cloud-based Power BI Service, where it can be managed, shared, and integrated into other platforms like company websites or portals. Step 1: Creating a Workspace A Workspace is a collaborative container in the Power BI Service where you house your reports, dashboards, and datasets. Sign in to the Power BI Service. On the left-hand navigation pane, click on Workspaces. Select Create a workspace (usually at the bottom of the pane). Give your workspace a unique name (e.g., "Sales Analytics
How to Publish a Power BI Report and Embed It on a Website
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I Connected 12 MCP Servers to Amazon Q. Here's What Broke
👋 Hey there, tech enthusiasts! I'm Sarvar, a Cloud Architect with a passion for transforming complex technological challenges into elegant solutions. With extensive experience spanning Cloud Operations (AWS Azure), Data Operations, Analytics, DevOps, and Generative AI, I've had the privilege of architecting solutions for global enterprises that drive real business impact. Through this article series, I'm excited to share practical insights, best practices, and hands-on experiences from my journey in the tech world. Whether you're a seasoned professional or just starting out, I aim to break down complex concepts into digestible pieces that you can apply in your projects. Let's dive in and explore the fascinating world of cloud technology together! 🚀 Written from experience building AI age
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I wrote a fused MoE dispatch kernel in pure Triton that beats Megablocks on Mixtral and DeepSeek at inference batch sizes
Been working on custom Triton kernels for LLM inference for a while. My latest project: a fused MoE dispatch pipeline that handles the full forward pass in 5 kernel launches instead of 24+ in the naive approach. Results on Mixtral-8x7B (A100): Tokens vs PyTorch vs Megablocks 32 4.9x 131% 128 5.8x 124% 512 6.5x 89% At 32 and 128 tokens (where most inference serving actually happens), it's faster than Stanford's CUDA-optimized Megablocks. At 512+ Megablocks pulls ahead with its hand-tuned block-sparse matmul. The key trick is fusing the gate+up projection so both GEMMs share the same input tile from L2 cache, and the SiLU activation happens in registers without ever hitting global memory. Saves ~470MB of memory traffic per forward pass on Mixtral. Also tested on DeepSeek-V3 (256 experts) and
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