Answers "can this hardware run model X at quantization level Y?" by
querying the HuggingFace Hub API for parameter count (safetensors) and
architecture (config.json), then applying the standard VRAM formula:
weights + KV cache (GQA-aware) + overhead. Reference algorithm from
LLMcalc (no code copied, unlicensed upstream).
Bump to 0.22.0.
Closes: #64
text.md: add LLM inference service section with three-path decision
table (GGUF/transformers/VLM mmproj/classifier), multimodal content-
block API, mock mode, CF_TEXT_URL wiring.
video.md: new file covering Marlin-2B service, server-local video_path
callout, CUDA 13 nightly path, trust_remote_code note, MIT/BSL boundary
(current wrapper is MIT; special sauce pipelines go in separate BSL
module, not cf-core).
mqtt.md: new file covering broker vs serial decision tree, MQTTClient
usage, TopicRouter.matches() NotImplementedError with workaround, install
extras.
Add circuitforge_core.memory module: MemoryClient wraps the mnemo HTTP sidecar
for entity / relation storage. All operations no-op gracefully when sidecar is
unavailable so products can import unconditionally. Adds optional [memory]
extras entry in pyproject.toml (mnemo-sdk>=0.1.0).
