Model Training for advanced learners
93 resources · page 3 of 3
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.
Fireworks AI documentation
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Fireworks AI documentation: serverless and dedicated inference, fine-tuning and deployment of open models.
Three Tests to Run Before You Switch from LoRA to FullFT (Fireworks AI)
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Fireworks AI post with controlled experiments on Qwen3.5-9B showing what to test before switching from LoRA to full fine-tuning.
GigaChat Family: Efficient Russian Language Modeling Through Mixture of Experts Architecture
by GigaChat team / SaluteDevices
Study a Russian-origin model team’s account of mixture-of-experts architecture, pretraining and evaluation.
Building an efficient harness for advanced enterprise work (Glean)
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Glean post on its coding-agent harness using code execution, tool-output truncation and progressive disclosure to cut tokens.
Grouped-Query Attention
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arXiv paper (2023) introducing grouped-query attention (GQA) and uptraining multi-head models to it.
LlamaFactory
by hiyouga (Yaowei Zheng) et al.
Compare adaptation methods within a shared training interface.
TRL - Transformers Reinforcement Learning
by Hugging Face
Find training interfaces for supervised and preference-based adaptation.
MindSpore 中文 API 文档
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MindSpore API documentation in Chinese for Huawei's deep learning training and inference framework.
Modal guides
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Modal guides: deploying model serving, GPU jobs and scalable applications on Modal's serverless platform.
Scaling to 1 million concurrent sandboxes in seconds (Modal)
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Modal engineering post on building a scheduler that runs one million concurrent code sandboxes per workspace.
Moore Threads docs center
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Moore Threads documentation centre for its Chinese GPUs and the MUSA SDK (in Chinese).
Reinforcement Learning from Human Feedback (book)
A conceptual path through reward modeling and post-training methods.
PEFT documentation
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Hugging Face PEFT documentation: parameter-efficient fine-tuning methods such as LoRA, with recipes.
Qiskit Machine Learning
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Qiskit Machine Learning documentation: quantum kernels and quantum neural networks for ML experiments.
QLoRA
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arXiv paper (2023) introducing QLoRA, fine-tuning 4-bit quantized LLMs with low-rank adapters.
Ray Serve documentation
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Ray Serve documentation: scalable library for building online inference APIs and composing model deployments.
Controlling Reasoning Effort in LLMs (Ahead of AI)
A free Ahead of AI article on training controllable reasoning effort.
SGLang docs: HiCache best practices
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SGLang HiCache best practices: configuring hierarchical KV caching across GPU, CPU and storage.
Tinker documentation
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Tinker documentation from Thinking Machines Lab: training API for fine-tuning open-weight LLMs, with SDK reference and recipes.
AdapTive-LeArning Speculator System (ATLAS) (Together AI)
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Together AI post on ATLAS, a speculative decoding system whose speculators keep learning from the live workload.
Cache-aware prefill-decode disaggregation (Together AI)
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Together AI post on cache-aware prefill-decode disaggregation for faster long-context inference serving.
Together AI documentation
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Together AI documentation for running, fine-tuning and serving open-source models.
Unsloth
by Unsloth
Study local training and adaptation workflows for supported models.
vLLM docs: Automatic prefix caching
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vLLM design document on automatic prefix caching and how shared prompt prefixes reuse the KV cache.
vLLM docs
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vLLM latest documentation: installation, serving, supported models and quantisation for the LLM inference engine.