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docs: update the packaged registry totals in the README

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@ -5,7 +5,7 @@
**Intelligent Ollama Model Selector**
AI-powered CLI that analyzes your hardware and recommends optimal LLM models.
Deterministic scoring across a packaged **multi-source registry** (Hugging Face + Ollama + GPT4All, **33k+ exact artifacts**) and the Ollama catalog, with live sync, runtime targeting, and hardware-calibrated memory estimation.
Deterministic scoring across a packaged **multi-source registry** (Hugging Face + ModelScope + Ollama + Docker Hub + GPT4All, **55k+ exact artifacts** from 10k+ repositories) and the Ollama catalog, with live sync, runtime targeting, and hardware-calibrated memory estimation.
[![npm version](https://img.shields.io/npm/v/llm-checker?style=flat-square&color=0066FF)](https://www.npmjs.com/package/llm-checker)
[![npm downloads](https://img.shields.io/npm/dm/llm-checker?style=flat-square&color=0066FF)](https://www.npmjs.com/package/llm-checker)
@ -39,7 +39,7 @@ Choosing the right LLM for your hardware is complex. With thousands of model var
| | Feature | Description |
|:---:|---|---|
| **200+** | Packaged Model Catalog | Ships with a synced Ollama SQLite catalog and can refresh from Ollama on demand |
| **33k+** | Multi-Source Registry | Exact installable/downloadable artifacts from Hugging Face, Ollama, and GPT4All with per-source commands and runtime targeting |
| **55k+** | Multi-Source Registry | Exact installable/downloadable artifacts from Hugging Face, ModelScope, Ollama, Docker Hub, and GPT4All with observed sizes, per-source commands and runtime targeting |
| **4D** | Scoring Engine | Quality, Speed, Fit, Context — weighted by use case |
| **Multi-GPU** | Hardware Detection | Apple Silicon, NVIDIA CUDA, AMD ROCm, Intel Arc, CPU, integrated/dedicated inventory visibility |
| **Calibrated** | Memory Estimation | Bytes-per-parameter formula validated against real Ollama sizes |