ML Journey

A practical guide to Matryoshka Representation Learning (MRL) for ML engineers: how nested dimension training works, fine-tuning MRL models with sentence-transformers MatryoshkaLoss, using truncated embeddings at inference time with correct renormalisation, building two-stage RAG retrieval with a small-dimension FAISS index for recall and full-dimension reranking for precision, benchmarking quality across dimensions on your own domain data, and how MRL compares to PCA-based dimensionality reduction.

A practical guide to monitoring Ollama with Prometheus and Grafana: what performance metadata the Ollama API exposes in every response, a Python Prometheus exporter that scrapes ollama ps and api tags, a FastAPI middleware proxy that intercepts requests and records request count, latency histograms, and token counters, Prometheus scrape configuration, and key Grafana dashboard PromQL queries for models loaded, VRAM usage, request rate, tokens per second, and P95 latency.

A practical guide to pytest for ML engineers: structuring test suites by speed and scope, shared fixtures for tiny models and small batches, testing data preprocessing deterministically, model shape and gradient flow tests, the overfit test for catching silent training bugs, loss function correctness tests, and configuring pytest markers and GitHub Actions CI to run fast unit tests on every push and GPU integration tests on a schedule.

A complete guide to building an Ollama-powered Telegram bot with python-telegram-bot: a basic bot that responds to messages with typing indicator, per-user conversation history with trimming, /start, /clear, and /model commands, restricting access to specific Telegram user IDs, and running the bot as a persistent systemd service alongside Ollama.

A practical guide to W&B Sweeps for ML engineers: how the sweep controller and agent architecture works, configuring Bayesian vs random vs grid search with the right parameter distributions, writing sweep-compatible training scripts that read from wandb.config, running parallel agents across multiple GPUs and SLURM clusters, using Hyperband early termination to save compute, interpreting parallel coordinates plots, and avoiding common pitfalls like over-broad search spaces and treating the best sweep run as a final result without seed averaging.

A complete guide to using Ollama as the AI backend in the Cursor code editor: configuring the OpenAI-compatible endpoint in Cursor settings, choosing fast small models for Tab completion versus larger models for Chat, creating a Modelfile with 16K context for better code responses, using Cursor Chat and Cmd+K inline editing with a local model, performance tips including model preloading and code-specialised model selection, and privacy implications of the fully local setup for proprietary codebases.

A practical guide to LLM token counting and cost estimation for ML engineers: accurate token counting with tiktoken for OpenAI models and the Anthropic token counting API for Claude, building a multi-provider cost estimator with current pricing, pre-flight checks to catch context window overflows and budget breaches before API calls, and a production logging wrapper for per-request cost attribution and identifying expensive outlier requests.

A practical guide to local image captioning and visual AI with Ollama: pulling LLaVA, moondream, and Gemma 3 vision models, captioning images with base64 encoding, visual question answering for receipts and diagrams, batch processing a folder of images to CSV, extracting text from photos with OCR-style prompts, structured image classification with Pydantic and Literal types, and a comparison of LLaVA vs moondream vs Gemma 3 for different vision tasks.

A practical guide to label smoothing for ML engineers: how soft targets prevent logit overconfidence, PyTorch implementation with nn.CrossEntropyLoss and a manual version for fine-grained control, the three settings where smoothing reliably helps (large-scale classification, seq2seq, small-data fine-tuning), why it actively hurts knowledge distillation, choosing smoothing values, and measuring calibration improvement with Expected Calibration Error.

A complete guide to using Ollama as the LangChain backend: installing langchain-ollama, using OllamaLLM and ChatOllama with system and human messages, building LCEL chains with prompt templates and StrOutputParser, a full RAG pipeline using OllamaEmbeddings with nomic-embed-text and Chroma vectorstore, adding conversation memory with InMemoryChatMessageHistory and RunnableWithMessageHistory, and creating a tool-using ReAct agent with LangGraph.