Machine Learning121 articles

Machine Learning

Articles

Fine-Tuning Frameworks for Open-Source LLMs in Production: Comparing Unsloth, Axolotl, LLaMA-Factory, and Torchtune
Open-source large language model post-training has fragmented into distinct engineering philosophies. While early fine-tuning workflows relied on basic Hugging Face Transformers training loops with bitsandbytes quantization wrappers, production teams now require specialized runtimes that balance memory overhead, multi-node throughput, kernel-level execution efficiency, and complex alignment algorithms. Four open-source frameworks dominate the production post-training landscape: Unsloth, Axolotl
August 29, 20261 min
Model Merging and Task Arithmetic: Mathematical Foundations, Task Vectors, TIES-Merging, DARE, and Spherical Linear Interpolation (SLERP)
Model Merging and Task Arithmetic: Mathematical Foundations, Task Vectors, TIES-Merging, DARE, and Spherical Linear Interpolation (SLERP) Model merging enables the combination of multiple specialized large language models into a unified checkpoint without retraining, backpropagation, or access to the original training datasets. Rather than running costly multi-task supervised fine-tuning across disparate domains, practitioners can directly manipulate parameter tensors in weight space to fuse ca
August 28, 20261 min
Kahneman-Tversky Optimization (KTO): Mathematical Foundations, Prospect Theory Utility, Binary Feedback Alignment, and Loss Aversion Dynamics
Kahneman-Tversky Optimization (KTO): Mathematical Foundations, Prospect Theory Utility, Binary Feedback Alignment, and Loss Aversion Dynamics Aligning large language models with human expectations has historically relied on pairwise preference data. Under traditional Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO), models learn from paired tuples where one completion is preferred over another for a given prompt. While mathematically grounded in the Bra
August 28, 20261 min
Mixture-of-Depths (MoD): Mathematical Foundations, Dynamic Token-Level Compute Routing, Top-k Capacity Budgeting, and FLOP-Optimal Transformer Architectures
Standard autoregressive Transformers allocate an identical compute budget to every token in a sequence. Regardless of whether a token represents a trivial punctuation mark, a common grammatical function word, or a complex semantic reasoning step, the model applies the exact same sequence of multi-head self-attention and multilayer perceptron (MLP) operations across all $L$ layers. In Mixture-of-Depths: Dynamically allocating compute in transformer-based language models, researchers at Google De
August 28, 20261 min
Simple Preference Optimization (SimPO): Mathematical Foundations, Reference-Free Implicit Reward, Length Normalization, and Target Margin Dynamics
Simple Preference Optimization (SimPO): Mathematical Foundations, Reference-Free Implicit Reward, Length Normalization, and Target Margin Dynamics Post-training preference alignment has become the definitive step in transforming raw pretrained large language models into instruction-following assistants. While Reinforcement Learning from Human Feedback (RLHF) via Proximal Policy Optimization (PPO) established the initial standard, its requirement to maintain actor, critic, reference, and reward
August 28, 20261 min
Contrastive Preference Optimization (CPO): Mathematical Foundations, Direct Likelihood Regularization, Out-of-Distribution Error Bounds, and Non-Degenerate Policy Alignment
Supervised fine-tuning (SFT) and preference alignment represent the standard two-stage training paradigm for post-training large language models. While SFT establishes foundational task adherence by minimizing cross-entropy loss against target references, it suffers from two structural limitations: it cannot surpass the quality ceiling of its training demonstrations, and it lacks an explicit mechanism to penalize near-correct outputs that contain subtle hallucinations or omissions. Direct Prefe
August 28, 20261 min
Post-Training Quantization Toolkits in Production: Comparing AutoAWQ, AutoGPTQ, bitsandbytes, and llm-compressor Architecture, Calibration Profiling, Kernel Execution, and Serving Economics
Large language model deployment in high-concurrency production environments is governed primarily by memory bandwidth and hardware economics. In standard autoregressive decoding, memory traffic dominates computation: for every token generated, every parameter of the model must be loaded from GPU High Bandwidth Memory (HBM) into SRAM and tensor cores. Serving a 70-billion parameter model in 16-bit precision requires at least 140 GB of VRAM solely for model weights, necessitating multi-GPU tensor
August 28, 20261 min
Activation Checkpointing and Selective Rematerialization: Mathematical Foundations, Sub-Linear Memory Scaling, Recomputation Trade-Offs, and Megatron-LM Attention Partitioning
Activation Checkpointing and Selective Rematerialization: Mathematical Foundations, Sub-Linear Memory Scaling, Recomputation Trade-Offs, and Megatron-LM Attention Partitioning Training modern large language models requires orchestrating hundreds of billions of parameters across distributed GPU clusters. While parameter counts and optimizer states are fixed for a given model architecture, the activation memory generated during forward passes scales directly with sequence length, batch size, and
August 28, 20261 min
Chinchilla Scaling Laws: Mathematical Foundations of Compute-Optimal Pre-Training, IsoFLOP Loss Profiles, Parametric Power Laws, and Data-Compute Allocation
Chinchilla Scaling Laws: Mathematical Foundations of Compute-Optimal Pre-Training, IsoFLOP Loss Profiles, Parametric Power Laws, and Data-Compute Allocation When allocating a fixed computational budget to train an autoregressive Transformer, engineers face a fundamental trade-off: should FLOPs be spent increasing the model parameter count ($N$), or should they be spent streaming a larger volume of training tokens ($D$)? For several years, frontier AI development followed the empirical scaling
August 28, 20261 min
Synthetic Data Generation and Data Curation Pipelines in Production: Comparing Distilabel, NVIDIA NeMo Curator, Data-Juicer, and InstructLab
As frontier language models exhaust human-authored web text, pretraining and post-training performance increasingly depends on automated data curation and synthetic data synthesis. Unfiltered public corpora introduce duplicate content, synthetic artifacts, low-reasoning text, and licensing liabilities. Modern foundation models, including Meta's Llama 3.1, NVIDIA's Nemotron-4 340B, Alibaba's Qwen 2.5, and IBM's Granite, are trained on datasets where synthetic generation and rigorous multi-stage f
August 27, 20261 min
Knowledge Distillation in Large Language Models: Mathematical Foundations of Forward vs. Reverse KL Divergence, Sequence-Level Distillation (SeqKD), and On-Policy Mode-Seeking Objectives
Knowledge Distillation in Large Language Models: Mathematical Foundations of Forward vs. Reverse KL Divergence, Sequence-Level Distillation (SeqKD), and On-Policy Mode-Seeking Objectives Deploying frontier large language models in latency-critical and compute-constrained environments remains an active challenge in artificial intelligence systems. While parameter scale correlates directly with downstream generalization and emergent reasoning capabilities, the memory footprint and FLOP intensity
August 27, 20261 min
Model Merging in Weight Space: Mathematical Foundations of Task Vectors, TIES-Merging, DARE, and Spherical Linear Interpolation
Training large language models across multiple downstream domains typically requires expensive multi-task supervised fine-tuning (SFT) or sequential continual learning. Both paths present steep practical challenges: multi-task training demands simultaneous access to massive, curated datasets and massive compute budgets, while sequential fine-tuning suffers from catastrophic forgetting. Model merging has emerged as a data-free, compute-efficient alternative. By combining the parameters of multip
August 27, 20261 min
Process Reward Models (PRMs) and Step-Level Verification: Mathematical Foundations, Intermediate Credit Assignment, Monte Carlo Value Estimation, and Search-Time Compute Scaling
Large language models have established chain-of-thought prompting as a standard paradigm for multi-step reasoning tasks across mathematics, formal logic, and software engineering. However, generating extended reasoning chains introduces a severe compounding error vulnerability: a single invalid deduction at an intermediate step invalidates all subsequent steps, even if the final generated tokens appear coherent. Evaluating and guiding these reasoning trajectories requires robust reward modeling
August 27, 20261 min
LLM Fine-Tuning and Post-Training Frameworks in Production: Comparing Unsloth, Axolotl, Torchtune, and LLaMA-Factory
Post-training has transitioned from a specialized research task into a standard production engineering discipline. As open-weight base models such as Llama 3.1, Qwen 2.5, and DeepSeek-V3 establish competitive baselines, the primary engineering challenge has shifted toward domain adaptation, instruction alignment, and reasoning distillation. However, selecting a post-training framework requires balancing competing architectural trade-offs: low-level kernel fusion, distributed multi-node scaling,
August 27, 20261 min
LLM Evaluation Frameworks in Production: Comparing DeepEval, Ragas, Promptfoo, and TruLens Architecture, Synthetic Data Generation, Judge Calibration, and CI/CD Automation
Automated evaluation is the primary engineering bottleneck in deploying reliable LLM applications. While traditional software engineering relies on deterministic unit and integration tests with binary pass/fail conditions, generative AI systems produce non-deterministic, open-ended natural language outputs. Relying on manual human review or ad-hoc prompting fails to catch regressions across prompt modifications, model version updates, and retrieval pipeline adjustments. To establish rigorous qu
August 27, 20261 min
LLM Guardrails and Runtime Safety in Production: Comparing NeMo Guardrails, Guardrails AI, Meta Llama Guard, and Lakera
LLM Guardrails and Runtime Safety in Production: Comparing NeMo Guardrails, Guardrails AI, Meta Llama Guard, and Lakera Deploying large language models (LLMs) into production environments introduces runtime risks that offline evaluation and static system prompts cannot eliminate. User-facing applications face prompt injections, jailbreaks, data exfiltration, toxicity, hallucinations, and malformed structured outputs. Relying solely on system prompt instructions ("You are a helpful assistant tha
August 27, 20261 min
FlashAttention: Mathematical Foundations, IO-Aware Tiling, Online Softmax Scaling, and Memory-Hierarchy Optimization in Transformer Architectures
Standard multi-head self-attention represents the core computational bottleneck in scaling modern Transformer models to long context windows. While the theoretical arithmetic operations of self-attention scale quadratically with sequence length $O(N^2)$, modern GPU execution profiles reveal that standard implementations are constrained not by floating-point arithmetic throughput (FLOPs), but by memory access latency and bandwidth between High Bandwidth Memory (HBM) and on-chip Static Random-Acce
August 27, 20261 min
Selective State Space Models (Mamba): Mathematical Foundations, Discretization Dynamics, and Linear-Time Sequence Modeling
The dominant paradigm in natural language processing and modern foundation models relies almost exclusively on the Transformer architecture. While standard multi-head self-attention delivers strong expressivity and in-context learning capabilities, its computational requirements present fundamental scaling bottlenecks: training complexity scales quadratically with sequence length $O(L^2)$, and autoregressive token generation requires storing key-value pairs in high-bandwidth memory (HBM), creati
August 27, 20261 min
Speculative Decoding: Mathematical Foundations, Rejection Sampling Dynamics, Draft Architectures, and Serving Latency
Autoregressive language models generate text sequentially, producing one token per forward pass. Because each forward pass must load hundreds of billions of parameters from high-bandwidth memory (HBM) into compute registers to calculate the next token for a small batch, decoding operates in a memory-bandwidth-bound regime with low arithmetic intensity. Speculative decoding resolves this bottleneck. Introduced independently by Leviathan et al. (2022) and Chen et al. (2023), the technique uses a
August 26, 20261 min
Direct Preference Optimization (DPO): Mathematical Foundations, Implicit Reward Derivation, Closed-Form Bradley-Terry Equivalence, and Reference Policy Regularization
Direct Preference Optimization (DPO): Mathematical Foundations, Implicit Reward Derivation, Closed-Form Bradley-Terry Equivalence, and Reference Policy Regularization Post-training preference alignment is the central mechanism for transforming pretrained base large language models into instruction-following, steerable, and safe conversational agents. Historically, the dominant paradigm for preference alignment has been Reinforcement Learning from Human Feedback (Christiano et al., 2017; Ouyang
August 26, 20261 min