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The author questions why tiny AI models with fewer than 50M parameters or swarms of specialized micro-models are rarely deployed in production, speculating on reasons like tooling biases or the convenience of generalist models.
A tweet from @tobi discusses how training tiny models for specialized use cases with a self-improving flywheel is highly effective, noting that Shopify ML team's finetuned 0.8b model outperforms GPT 5.6-sol xhigh in a specific task.
TwiL-LM, a family of tiny specialized open models, is released on HuggingFace. The 3B version outperforms OpenAI's 120B gpt-oss on formal reasoning benchmarks and runs efficiently on consumer hardware.
A developer demonstrates running a 28.9 million parameter language model on an $8 ESP32-S3 microcontroller using Google's Per-Layer Embeddings to store most parameters in flash, achieving around 9.5 tokens per second on-device text generation.
SupraLabs releases reasoning-corpus-4K-5M-v1, a 5M-sample reasoning dataset for training small language models (SLMs), featuring chain-of-thought traces and ChatML format, hosted on Hugging Face.
TinyRouter is a tiny 10K-parameter LLM router that learns to route each question to the best specialist model from a pool of open-source LLMs, using evolutionary training. It achieves performance matching or exceeding individual models on MMLU and math benchmarks.
A student introduces Silia, a novel transformer architecture that combines attention and FFN into a unified operation to save parameters at scales ≤10M, achieving comparable performance to GPT-2 with fewer parameters despite limited compute resources.
Announces an upcoming video on training tiny models for preference tuning, covering reward models, RLHF, DPO, ORPO with Unsloth and TRL.
This paper presents Flavors of Moonshine, a suite of tiny specialized ASR models for edge devices. The authors show that monolingual models trained on a balanced mix of human-labeled, pseudo-labeled, and synthetic data outperform larger multilingual models like Whisper, achieving state-of-the-art error rates for small models and enabling on-device ASR for underrepresented languages.