Machine Learning
Deep dives into ML algorithms, models, and applications
Scikit-learn in 2026: Still Relevant and What's New in v1.4+
Scikit-learn remains the best library for classical ML on tabular data - v1.4+ adds HDBSCAN, TunedThresholdClassifierCV, and better Pipeline verbosity while staying beginner-friendly.
SOLAR 10.7B: How Depth Upscaling Makes a 10B Model Beat 30B Models
Upstage's SOLAR 10.7B uses depth upscaling - duplicating and fine-tuning Llama 2 layers - to create a model that outperforms 30B-class models on the HuggingFace leaderboard while remaining practical to serve.
Kosmos-2: Grounded Image Understanding That Links Text to Image Regions
Microsoft's Kosmos-2 produces bounding box coordinates inline with its text output, connecting every noun and phrase in its response to a specific region of the image.
SmolLM2: Training Tiny LLMs That Actually Work on Device
HuggingFace's SmolLM2 family (135M/360M/1.7B) brings capable instruction-following LLMs to browser and edge environments through WebGPU inference and Transformers.js, with quality trained on carefully curated educational data.
Phi-3 Vision: Microsoft's 4.2B Multimodal Model for Edge Devices
Phi-3 Vision packs chart understanding, document analysis, and image reasoning into 4.2 billion parameters - small enough to run on a mobile device with CoreML or ONNX, yet scoring 59.8% on MMMU.
SDXL-Turbo: Real-Time Image Generation in 1-4 Steps with Adversarial Diffusion Distillation
A technical deep-dive into SDXL-Turbo and Adversarial Diffusion Distillation (ADD). Learn how to collapse 50 diffusion steps into sub-30ms real-time generation with runnable PyTorch code, VRAM profiles, and production benchmarks.
RoBERTa: BERT Done Right - When and How to Use It for Classification
RoBERTa improves on BERT through better pre-training - dynamic masking, no next-sentence prediction, larger batches, and more data - delivering consistent GLUE leaderboard advantages for classification tasks.
NVIDIA Nemotron-4 340B and Llama-3.1-Nemotron-70B: Enterprise LLMs From NVIDIA
NVIDIA entered the foundation model market with two distinct plays: Nemotron-4 340B for synthetic data generation pipelines, and Llama-3.1-Nemotron-70B-Instruct with an Arena Hard score of 85.1% for enterprise inference.
OpenAI Fine-Tuning API: Architecture, Cost Economics & Production Guide
OpenAI's fine-tuning API lets you customize GPT-4o mini and GPT-3.5 Turbo with your own data - but the economics only make sense in specific scenarios.
CogVLM2: Open-Source Video and Image Understanding With Long Context
Zhipu AI's CogVLM2 introduces a Visual Expert Module that gives visual tokens their own weight matrices, enabling richer image and video understanding than shared-weight alternatives.
Moondream2: A 1.9B VLM That Runs on a Raspberry Pi
Moondream2 is a 1.9B parameter vision-language model that fits in 1.2GB RAM when quantized, enabling image captioning, visual Q&A, and object detection on embedded hardware and edge devices.
Phi-3 Mini: Running a 3.8B Parameter LLM On Your Phone
Phi-3 Mini at 3.8B parameters outperforms Mixtral 8x7B on several benchmarks and runs in browsers via WebGPU or on Android/iOS via ONNX. Here's how.
InternVL 2: The Open-Source VLM Approaching GPT-4V on Benchmarks
Shanghai AI Lab's InternVL2-26B scores 61.2% on MMMU - within 2 points of GPT-4V - using a 6B vision encoder and dynamic high-resolution image tiling.
DistilBERT in Production: Fast NLP Classification Without the GPU Bill
DistilBERT delivers 97% of BERT's performance at 40% smaller size and 60% faster inference, making it the practical default for production text classification that needs low latency on CPU.
Idefics2: HuggingFace's Open Multimodal Model Built on Mistral and SigLIP
Idefics2 is an 8B open multimodal model that handles interleaved image-text sequences, arbitrary image resolutions, and fine-tuning for document and chart understanding.
ColPali: Visual Document Retrieval Without OCR Using Late Interaction & Vision LLMs
ColPali treats PDF pages as images and uses vision-language embeddings with ColBERT-style late interaction scoring, bypassing the lossy OCR pipeline that breaks table, chart, and layout-heavy document retrieval.
Sentence Transformers: The Go-To Library for Text Embeddings in 2026
The Sentence Transformers library provides a unified interface for generating text embeddings, enabling semantic search, clustering, and fine-tuning on custom similarity tasks with minimal code.
Cohere Embed v3: Multilingual Embeddings Built for Enterprise RAG
Cohere's Embed v3 introduces a critical input_type parameter that tells the model whether it's encoding a query or a document - a distinction that meaningfully improves retrieval precision in production RAG pipelines.
PaliGemma: Google's Compact Vision-Language Model for Fine-Tuning
PaliGemma combines SigLIP vision encoding with Gemma 2B language generation in a 3B model explicitly designed to be fine-tuned rather than used zero-shot.
SAM 2: Architecture, Video Segmentation Memory & Production Implementation Guide
SAM 2 extends the original Segment Anything Model to video with a streaming memory bank, enabling zero-shot object tracking and segmentation across arbitrary video sequences.
OpenAI text-embedding-3: The New Embedding Models and When to Use Each
OpenAI's text-embedding-3-small and text-embedding-3-large introduce Matryoshka representation learning - you can truncate dimensions without retraining, cutting storage costs while keeping most retrieval quality.
Gemini 1.5 Pro: Working With 1 Million Token Context Windows
Gemini 1.5 Pro offers a 1 million (and experimental 2 million) token context window with 99%+ needle-in-haystack recall. Here's how to use it for long-context tasks.
BGE-M3: The Embedding Model That Does Dense, Sparse, and Multi-Vector Retrieval
BGE-M3 from BAAI unifies three retrieval paradigms in one model - dense vectors, sparse keyword matching, and ColBERT multi-vector scoring - across 100+ languages with 8192 token support.
Axolotl: YAML-Configured LLM Fine-Tuning for Any Dataset Format
Axolotl lets you fine-tune any open-source LLM using a single YAML config file - no Python required - with support for LoRA, QLoRA, and multi-GPU training.
