Supercharge Your Model Training
Efficient Deep Learning Systems course materials (HSE, YSDA)
From teacher to tiles — a from-scratch LLM distillation & serving engine: custom Triton/CUDA kernels, FSDP distillation, paged-KV continuous batching, speculative decoding, a Rust gateway, a JAX oracle, and interpretability tooling.
Run more RL experiments. Wait less for GPUs.
Multimodal RAG system with CLIP/text embeddings, rank-fusion reranking, FastAPI serving, and Recall@5 evaluation.
Designing IT and ML Applications using Systems Thinking Approach at IIT Bhilai (CS559)
A curated list of resources for ML Systems Engineering - hardware, compilers, distributed training, inference, and production operations.
Structured notes on designing scalable and fault-tolerant ML systems, to refresh your knowledge and help you prepare for a system design interview. Covers system design, MLOps, and case studies.
LICITRA v1 — superseded by licitra-mmr-core
LICITRA v1 evidence — superseded by licitra-mmr-evidence
One small, honest ML system per day, built end to end on Hopsworks.
End-to-end personalized feed ranking system demonstrating retrieval → ranking pipelines, offline evaluation, realistic simulation, and business-aligned diagnostics inspired by large-scale social platforms.
Public architecture and product documentation for Anion — a local AI operating layer for the Linux desktop.
cloud-native machine learning platform for real-time inflation nowcasting
Scalable Training Telemetry and Metrics Visualization
ML Systems Reproducibility Auditor — Analyze GitHub repositories for reproducibility, benchmarking rigor, and distributed training design quality.
Quantization research for compiler-verified LLM systems. Three arms: (A) Phase D IntLLM 1.58-bit ternary, 3 gates PASS; (B) v3.1 adaptive KV cache quant, 7/9 cells; (C) Phase E bilingual ID+EN, corpus v1.0 25.67B tokens, E2.4 honest negative result. In-kernel via FajarOS Nova. Apache 2.0. Made in Indonesia.
Distributed training profiler for analyzing compute, communication, memory, and scaling bottlenecks in ML training systems.
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