ThingsAI

Building efficient, specialist Small Language Models that run on consumer hardware. Zero telemetry. Open weights. Everything from tokenizer to training script is public.

Models

• Dwarf-15M A 15.54M parameter shell/bash specialist. 12 layers, d_model=320, GQA 5Q/1KV, SwiGLU, RMSNorm, RoPE. Custom 8202-token vocabulary via DwarfGoToken. 1390:1 token-to-parameter ratio across 11 datasets spanning raw shell, Python, C, instruction pairs, and English web text. Target use case: CLI tool that translates natural language into bash commands with user review before execution.

• Quark-270M Our largest model. 252M effective parameters, 32 layers, d_model=768, GQA 12Q/4KV, 65K bilingual vocabulary (Italian + English). Trained on curated multilingual data. Available as Base and Instruct variants.

• Quark-135M Bilingual (Italian + English) general-purpose model. 135M parameters, 30 layers, 9 attention heads (3 KV, GQA), SwiGLU, RMSNorm, RoPE θ=10k. Trained on 15B+ tokens. Published benchmarks: HellaSwag 31.37%, ARC-Easy 41.46%, PIQA 61.26%.

Links

• Models and tokenizers: HuggingFace
• Script & Tool: GitHub
• Website: things-ai.org
• GoToken: crates.io · PyPI