A language model can become a fast multiple-choice decision model by scoring only the allowed answer tokens. This guide shows how to read next-token logits, handle tokenization, evaluate accuracy, and calibrate overconfident probabilities with temperature scaling.
AI agents need reliable choices as often as they need fluent text. This guide explains how Cloudflare’s Clef decision models return typed probabilities, why their non-autoregressive design is fast, and how reinforcement learning can adapt them to specialized workflows.
Jeff turns Qwen3.5 and Gemma 4 into small decision models that classify plain-language situations without generating a paragraph. The 0.8B version reports probabilities in roughly 22–28 ms on listed GPU and Apple silicon hardware, while local fine-tuning can adapt it to specialized tasks.
A chat template can change whether an LLM responds with “I’m just an AI” or uses more experiential language. Activation steering experiments suggest this voice is partly a deployment effect, not a literal report about the model’s inner life.
An ordinary language model can act like a fast decision function when you force its next token to be one of a few labels and read the returned log probabilities. This guide builds a Python wrapper for choices, yes/no checks, scores, webcam images, caching, and calibration pitfalls.
An apparently silly GET-only service reveals how model weights move from one machine to another. This guide explains chunks, Base64, GGUF, llama.cpp, and the security problems created when URLs perform writes and inference.
Building an LLM from scratch is less about matching frontier labs and more about understanding every layer: tokenization, Transformers, training loops, data quality, evaluation, and hardware. A practical path starts with a tiny character model and grows through careful experiments.
Turbovec is a Rust vector index built on Google’s TurboQuant. It stores high-dimensional embeddings in 2–4 bits per coordinate, avoids a separate training phase, and uses SIMD-friendly layouts for fast similarity search.
Qwen 3.8 27B’s default `xhigh` reasoning can turn simple prompts into slow, highly elaborated outputs. Learn how thinking mode and `reasoning_effort` interact with context windows so local runs stay responsive.
Qwen3.8-27B-FP8 packages a 27B vision-language model as fine-grained FP8 (block size 128) weights. It adds practical controls like `reasoning_effort` and `preserve_thinking`, plus native 262K context with extensibility toward 1M tokens for long-horizon tasks.