small-models
A 150M model set an ARC-AGI record for cost, not score News
Pathway's BDH-CQ solves reasoning puzzles by iterating in a latent workspace instead of writing out its thinking, reaching 29.5% on public ARC-AGI-1 at seven hundredths of a cent per task and setting a record in cost efficiency rather than accuracy.
Liquid Shipped a 2.6B Tool-Calling Model and Told You Not to Code With It News
Liquid AI released LFM2.5-2.6B, a small model whose stack is mostly cheap convolution layers with attention rationed to eight of thirty, built for tool use and long context - and whose own model card says not to use it for agentic coding or knowledge-heavy work.
An $8 Microcontroller Now Runs a 28.9-Million-Parameter Language Model News
A developer fit a 28.9-million-parameter model onto an ESP32-S3 with 512KB of fast memory by keeping most of the model in flash storage and reading only about 450 bytes of it per word generated.
Program-as-Weights compiles a plain-English spec into a tiny model you run on a laptop News
A new method called Program-as-Weights uses a 4-billion-parameter 'compiler' to turn a natural-language task description into a small weight file that a frozen 0.6B model runs, matching a 32B model's quality while using about one-fiftieth the memory and running at 30 tokens a second on a MacBook.
Ternary Bonsai models Tool
A family of 1.7B, 4B and 8B models built for extreme quantization, shipped in the official group-64 two-bit format that mainline llama.cpp reads. Useful if you want to see what 2-bit inference feels like without converting anything yourself.
Program-as-Weights Tool
Turns a plain-English task spec into a small weight file that a frozen 0.6B model runs locally -- matching a 32B model's quality at roughly one-fiftieth the memory and about 30 tokens/sec on a MacBook M3. Open repo and site for compiling cheap, offline 'fuzzy' text programs.
MiniCPM5-1B Tool
OpenBMB's dense 1B local model with Think and No-Think modes, trained with SFT, RL, and on-policy distillation. Designed for on-device and edge deployment.
LFM2.5-2.6B Tool
Liquid AI's 2.7B tool-calling model with a 128k context, built as 22 short-convolution layers plus 8 grouped-query-attention layers so most token mixing stays local and cache-friendly. Post-trained inside real agent harnesses for tool use, extraction, retrieval and long-context workflows. The model card explicitly recommends against agentic coding and knowledge-heavy tasks, and it always enters a reasoning mode before answering.