Run Merlinite 7B on Mac
Merlinite 7B runs 100% private on Mac inside Private LLM — no internet connection required, no data sent to any server.
Download the quantized weights
These are the exact OmniQuant weights Private LLM runs for Merlinite 7B, published on our Hugging Face org. They're standard weights you can load in any app that supports the format, not just Private LLM.
Specifications
- Parameters
- 7.2B
- Context window
- 33K tokens
- License
- Apache 2.0
- Quantization
- OmniQuant 4-bit
- Family
- Mistral 7B
What Merlinite 7B is good at
Merlinite 7b is a language model built on Mistral 7B v0.1 using a training method called LAB from IBM Research. It is designed to follow instructions and handle tasks like answering questions, reasoning, and creative writing. The model performs well on benchmarks compared to similar models, especially in areas like general knowledge and math problem solving.
Which of your devices can run it
Mac
How to run Merlinite 7B in Private LLM
- Download Private LLM from the App Store.
- Open the in-app model library and choose Merlinite 7B.
- Download the model once, then chat fully offline.
Variants & related models
Frequently asked questions
Merlinite 7B is too large for current iPhones. It runs on Mac with enough unified memory inside Private LLM, fully offline.
Yes. Merlinite 7B runs on Macs with enough unified memory, such as Mac (Apple Silicon, 192GB), MacBook Pro (M4 Max, 128GB), Mac Studio / Pro (Apple Silicon, 96GB), MacBook Pro (M4 Max, 64GB), MacBook Pro (M4 Max, 48GB), MacBook Pro (M4 Max, 36GB), Mac (Apple Silicon, 32GB), MacBook Air (M4, 24GB), MacBook Air (M-series, 16GB), Mac (Apple Silicon, 8GB), fully on-device in Private LLM.
Yes. Once downloaded in Private LLM, Merlinite 7B runs 100% on-device — no internet connection, and nothing is sent to any server.
Private LLM is a one-time purchase with no subscription and no per-message cost. The models themselves are open source — once downloaded, they run offline with nothing to pay per use.
Why run Merlinite 7B in Private LLM
Private LLM has run local AI on iPhone, iPad, and Mac since 2023, before Apple Intelligence existed. Inference happens on your device, so your Merlinite 7B conversations never reach a server. The part most apps gloss over is quantization, and that is exactly where on-device quality is won or lost. Most llama.cpp and MLX wrappers ship the same off-the-shelf 4-bit RTN weights. Private LLM ships GPTQ and OmniQuant quantization, tuned per model, and our 3-bit OmniQuant models match or beat those 4-bit RTN builds on the same Apple Silicon. Run the same model both ways and you feel it in the first reply. See how our quantization works.
Specifications and summary come from Merlinite 7B's Hugging Face model card, released under the Apache 2.0 license. Private LLM ships its own quantized models, built with OmniQuant quantization tuned per model, and isn't affiliated with the model's authors.