I’m releasing DeepBrainz-R1 — a family of reasoning-first Small Language Models (SLMs) designed for agentic systems in production.
The core idea is simple: agentic systems don’t ask once — they reason repeatedly (tool calls, verification loops, retries, schema-constrained outputs). That changes reliability and cost requirements, where large chat-optimized LLMs often struggle.
DeepBrainz-R1 models are post-trained to improve multi-step reasoning behavior, output stability, and robustness under agentic workloads. The focus is not chat or creative writing, but predictable reasoning at small parameter sizes.
Models: - R1-4B (flagship) - R1-2B (lower latency / cost) - R1-0.6B-v2 (small, local / edge agents) - Experimental long-context variants (16K / 40K)
Everything is open (Apache-2.0). Community-maintained quantizations (GGUF, low-bit) are already appearing.
I’d love feedback from people building agents, tool-using systems, or long-running reasoning pipelines.