mi12-root

Thanks for sharing the details—that makes a lot of sense. Fine-tuning and exporting models on-device can be tedious nowadays. We’re planning to look into supporting popular on-device LLM models more directly, so deployment feels much easier. We'll let you know here or reach out to you once we have something

I tested the system LLM with a long article using two prompts: one asking for a summary in at most 20 words, and another asking for a one-sentence summary. In both cases, the model followed the instructions correctly. Regarding your second point in the link above: maximumResponseTokens: 500 corresponds to roughly 1,500–2,000 characters in English. For the AFM tokenizer, a token typically represents 3–4 characters. Could it be why you are getting large outputs? If you share your prompt(s), we’d be happy to take a closer look. You can reach us on Slack, Discord, or privately at [email protected]

While we did use AI coding tools, we put significant thought into the design of SwiftAI and would greatly value feedback on it (see system design doc above). All AI-generated code was carefully reviewed and often, rewritten (no yolo)

That’s a good suggestion, and it indeed sounds like something we’d want to support. Could you help us better understand your use case? For example, where do you usually get the models (e.g., Hugging Face)? Do you fine-tune them? Do you mostly care about LLMs (since you only mentioned llama.cpp)?

Thanks for the feedback! When you say “custom,” do you mean additional integrations with LLM providers, or more documentation on how to build your own custom integration? If you mean the former, we’re currently focused on stabilizing the API and reaching feature parity with FoundationModels (e.g., adding streaming). After that, we plan to add more integrations, such as Claude, Gemini, and on-device LLMs from Hugging Face.

We’ve begun internally evaluating the model and will share our findings more in details later. So far, we’ve found that it performs well on tasks such as summarization, writing, and data extraction, and shows particular strength in areas like history and marketing. However, it struggles with STEM topics (e.g., math and physics), often fails to follow long or complex instructions, and sometimes avoids answering certain queries. If you want us to evaluate a certain use case or vertical, please share it with us!