Model Training
Study the data, algorithms and infrastructure used to change model weights.
- Pretraining (3)
- Training Data (1)
- Post Training Rl (5)
- Fine Tuning (4)
- Training Infrastructure (123)
136 resources · page 3 of 5
Fireworks blog
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Fireworks AI blog on inference performance, fine-tuning and model releases on its serving platform.
google-ai-edge/gallery: skills
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Google AI Edge Gallery skills folder on GitHub: skills for on-device ML and generative AI apps.
Google TPU docs
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Google Cloud TPU documentation for running machine-learning training and inference on Tensor Processing Units.
GPU MODE
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GPU MODE site: community for learning GPU programming through lectures, hackathons, kernel competitions and working groups.
GPU MODE lectures
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GPU MODE lectures GitHub repo: slides and code from community lectures on GPU programming and CUDA kernels.
GPU MODE resource stream
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GPU MODE resource-stream GitHub repo: curated links to CUDA and GPU programming books, papers, posts and videos.
Groq blog
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Groq blog with posts on its LPU inference hardware, cloud API and supported models.
Groq documentation
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Groq documentation: OpenAI-compatible API, available models and rate limits for its LPU inference service.
Making Deep Learning Go Brrrr From First Principles (Horace He)
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Horace He's Making Deep Learning Go Brrrr: first-principles guide to whether models are compute-, memory- or overhead-bound.
vLLM documentation
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vLLM documentation: open-source engine for high-throughput LLM inference and serving.
LM Studio blog
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LM Studio blog with release notes and technical posts on running LLMs locally.
Modal blog
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Modal blog on serverless GPU computing, infrastructure engineering and running AI workloads.
Ollama blog
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Ollama blog with release notes, supported model announcements and guides for running models locally.
OpenRouter docs: Prompt Caching
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OpenRouter guide to prompt caching across model providers, with cost implications.
PyTorch FSDP tutorial
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PyTorch tutorial on Fully Sharded Data Parallel (FSDP2) for training large models across GPUs.
Qwen 中文文档
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Qwen documentation in Chinese: usage, deployment and fine-tuning of Qwen language and multimodal models.
SGLang documentation
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SGLang documentation: serving framework for large language and multimodal models.
Together AI blog
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Together AI blog with product launches, research and technical guides on inference, fine-tuning and GPU clusters.
Transformers documentation
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Hugging Face Transformers documentation: reference for model architectures, loading, training and generation.
Triton Puzzles
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Triton Puzzles GitHub repo: interactive exercises for learning to write GPU kernels in Triton.
Triton tutorials
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Triton tutorials: ordered examples from vector addition to fused softmax, matmul and fused attention GPU kernels.
Unsloth blog
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Unsloth blog with guides and release notes for fast, memory-efficient LLM fine-tuning.
ruGPT training and fine-tuning examples
Inspect Russian-model fine-tuning and pretraining scripts, with explicit warnings about the legacy environment.
A graph placement methodology for fast chip design
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Nature paper (2021) by Mirhoseini et al. introducing the reinforcement learning chip floorplanning method later named AlphaChip.
Agentic inference optimization (Baseten)
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Baseten post on optimising inference engines for agent workloads, reporting decode-speed and latency gains over vLLM.
Baseten documentation
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Baseten documentation for running hosted models, deploying custom models and training on GPU infrastructure.
BentoML documentation
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BentoML documentation: open-source inference platform for packaging, deploying and scaling AI models.
Code execution with MCP
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Anthropic post on code execution with MCP: agents write code to call tools, reducing token use and context load.
Defeating Nondeterminism in LLM Inference
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Thinking Machines Lab post on the causes of nondeterminism in LLM inference and batch-invariant kernels to fix it.
DoWhy documentation
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DoWhy documentation: Python library for causal inference with modelling, identification, estimation and refutation steps.
EconML
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EconML documentation: Python methods for estimating heterogeneous treatment effects with machine learning.
Feast documentation
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Feast documentation: open-source feature store for serving consistent features to ML training and inference.