anuj0456

the only way i am aware of is to cross check with what deployed on huggingface/transformers repo

yes, that is correct. after chinca came into picture the advancement in this field sky rockted

fixed

i will see what i can add here. contributions are welcome

thanks for pointing out. its a bug

Thanks for sharing

yes, most of them are similar. but implementation of GQA, MLA, mHC, Sliding Window changes the implementation drastically because of which the overall model effeciency changes.

yes. this is just raw implementation of the model arch as described in papers. for complete model training with back propogation we need training pipeline with optmizer and loss calculation.

will try my best, but open for contribution.

Thanks, Yes PyTorch is a framework largely used to create neural network models.

I have been studying modern LLM architectures and started implementing them from scratch in PyTorch to better understand the design choices behind each model.

OpenArch is a collection of these implementations, including Llama, Qwen, DeepSeek, Gemma, Kimi, GPT-OSS and others.

The goal is to keep the code readable and useful as a reference when going from the paper to an actual implementation.

Would be interested in feedback from people working on model architecture and training.

Sure, we will add comparison matrices. this is helpful

thanks. this is helpful. will update the UI

the js was not mapped correctly. fixed now

Key Features

Hierarchical Agent System: Manager/worker hierarchies, intelligent task routing, and specialized agent implementations

Production Ready: Asynchronous processing, dynamic scaling, load balancing, and fault tolerance

Advanced Memory: Semantic storage, task history tracking, and context preservation

Integrations: Support for multiple LLM providers (OpenAI, Anthropic, Google)

The framework includes pre-built specialized agents for customer service, document processing, email handling, research, marketing, sales, social media, and web search.

I built PilottAI to address the challenges of orchestrating multiple AI agents in production environments. I'd love feedback from the HN community, especially from those working with multi-agent architectures.

try again please.

Built for creating autonomous multi-agent systems with enterprise-ready features. It provides advanced orchestration capabilities for building AI applications that can scale.