liquid foundation models - config files in Liquid Foundation Models - code from scratch - part 2

Описание: 🎬 Part 2 – Config Files in Liquid Foundation Models

In Episode 2 of my LFM2 from-scratch series, we dive into the configuration files that drive Liquid Foundation Models. I’ll explain how the model’s hyperparameters, architecture settings, and inference options are defined — and then we’ll build a config system in code.

In this video you’ll learn:

What a config file is and why it's critical for defining LFM2 model behavior

Key hyperparameters in LFM2 config: model size (350M / 700M / 1.2B), block structure (convs vs attention), context length (e.g. 32K)
Toolify
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Inference-related settings: sampling temperature, repeat-penalty, min-p sampling (as seen in LM Studio config for LFM2-1.2B)
LM Studio
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How config ties into Liquid's architecture: how the config reflects LFM2’s hybrid structure (short convolutions + grouped-query attention)
Liquid AI
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Writing your own config parser / manager in Python: load, validate, and use config values in your model code

Best practices: how to design configs that are flexible and maintainable, especially for edge / on-device deployment

Why this matters:
Config files are more than just “settings” — they define how your model is built and behaves. By coding your own config system, you gain control and understanding over LFM2’s structure and performance, which is essential if you want to fine-tune, customise, or deploy your own version of the model.

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