Training

Posts that use this term

• Troubleshooting local LLMs and keeping up

  The catalog of common local-LLM failures: OOM, slow tok/s, garbage output, instruction drift, RAG miss, tool-call hallucination. Plus where to follow the field as it moves.

• Fine-tuning a model locally

  When fine-tuning is the right answer (rarely) and how to do it on consumer hardware: LoRA, QLoRA, MLX-LM, Unsloth. A worked example fine-tuning Llama 3.2 3B on a 16GB Mac.

• Local RAG and embeddings

  A complete local RAG pipeline in 30 lines: nomic-embed-text for embeddings, Chroma for the vector DB, Llama 3.2 for the chat model. Why local RAG often beats cloud RAG for personal knowledge bases.

• Integrating a local LLM into your workflow

  Wire your local LLM into VS Code (Continue, Cline), web UIs (Open WebUI, LibreChat, Page Assist), and your own apps via the OpenAI-compatible API. The swap-cloud-for-local pattern in real codebases.

• Picking a local model by task

  The 2026 open leaders by task: coding (Qwen 2.5 Coder, DeepSeek-Coder), chat (Llama, Qwen, Mistral), small-model renaissance (Phi-3, Gemma 2), structured output, multimodal, embeddings.

• Quantization, distillation, pruning: making models fit

  Three ways to shrink an LLM. Quantization (Q2-Q8 with K-quants in GGUF), distillation (teacher to student), pruning. Why Q4_K_M is the community default and what each lever costs.

• The pitch for local LLMs in 2026

  Why every engineer should run a local LLM in 2026: privacy, zero marginal cost, lower latency, no rate limits, and offline. Even a 16GB MacBook Air runs Llama 3.2 3B at 30 tok/s.

• What leaves your machine when you use AI

  What providers actually see, log, and retain when you call an LLM API in 2026. What 'we don't train on your data' really means, free vs paid tier differences, and when local is the only safe option.

• The runtimes: llama.cpp, Ollama, LM Studio

  llama.cpp is the engine; Ollama and LM Studio wrap it. What each does, when to pick which, and why the OpenAI-compatible APIs are mostly but not entirely interchangeable.

• Why Apple Silicon punches above its weight on local LLMs

  Unified memory means the GPU sees all of RAM. Why that beats discrete-GPU PCs above 32B parameters, what fits in 16/32/64/128/192GB, and where Apple Silicon still loses.

• Prompt, RAG, fine-tune: three ways to shape a model

  Three levers for shaping what an LLM does: prompting (ask better), RAG (give it the right context), fine-tuning (change the weights). What each costs, what each fixes, and how to pick.

• RAG: giving a model memory it doesn't have

  RAG is the pattern of fetching relevant text from a search system and putting it in the LLM's context window before asking your question. Not magic, not fine-tuning — just better prompts.

• The context window, and why models hallucinate

  An LLM only sees a fixed-size slice of text at a time. When it doesn't know something, it predicts anyway — that's a hallucination, not a bug.

• From models to LLMs

  An LLM is one kind of ML model — trained on text, predicts the next token. That single trick at scale gets you ChatGPT, and also explains where it breaks.

• How a model learns: training and inference

  Training is the expensive one-time event where a model's numbers get tuned. Inference is the cheap repeated use afterwards. The gap in cost is enormous, and it shapes the whole industry.

• What makes a model: data and algorithm

  A model is a file of learned numbers, produced by running an algorithm over data. Both ingredients matter, but bad data beats a good algorithm every time.

• Inside AI: machine learning and deep learning

  Open the AI umbrella. Machine learning is the part that learns from data. Deep learning is ML done with neural networks — and that's where today's models live.