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| title: README | |
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| short_description: Reasoning-first, agentic small language models (SLMs). | |
| # DeepBrainz AI & Labs | |
| **Reasoning-first Small Language Models for agentic systems in production** | |
| DeepBrainz AI & Labs builds **reasoning-first, agentic Small Language Models (SLMs)** optimized for **reliability, controllability, and efficiency** in real-world AI systems. | |
| We focus on **behavioral intelligence** — training models to reason, plan, and act — rather than scaling parameters or gaming benchmarks. | |
| --- | |
| ## 🔑 Start Here (Recommended Models) | |
| If you’re new to DeepBrainz-R1, start with one of these: | |
| - **[DeepBrainz-R1-4B](https://huggingface.co/DeepBrainz/DeepBrainz-R1-4B)** — flagship model | |
| Best overall reasoning quality and stability for production agentic systems. | |
| - **[DeepBrainz-R1-2B](https://huggingface.co/DeepBrainz/DeepBrainz-R1-2B)** — balanced model | |
| Strong reasoning with lower latency and cost. | |
| - **[DeepBrainz-R1-0.6B-v2](https://huggingface.co/DeepBrainz/DeepBrainz-R1-0.6B-v2)** — small & efficient | |
| Designed for local inference, edge agents, and cost-sensitive workflows. | |
| > All other variants are **experimental or research-only**. | |
| --- | |
| ### 🧠 Capabilities | |
| #### What DeepBrainz-R1 Is Built For | |
| - Multi-step reasoning | |
| - Tool-calling and agent loops | |
| - Long-context analysis | |
| - Deterministic, inspectable behavior | |
| ### 🚫 What It Is *Not* Optimized For | |
| - Open-ended chat or roleplay | |
| - Creative writing | |
| - Prompt-memorization benchmarks | |
| --- | |
| ## 🧪 Research Philosophy | |
| We explicitly optimize **against**: | |
| - Shallow pattern matching | |
| - Benchmark gaming | |
| - Prompt memorization | |
| We treat intelligence as a **behavior to be trained**, not a side-effect of model size. | |
| --- | |
| ## What We Work On | |
| We focus on **small, efficient language models** that demonstrate strong reasoning behavior without relying on brute-force scale. | |
| Our research explores: | |
| - Reinforcement learning–based post-training | |
| - Test-time and inference-time scaling | |
| - Long-context efficiency | |
| - Agentic reasoning workflows | |
| - Systematic ablations over architecture, data, and context length | |
| --- | |
| ## DeepBrainz-R Series | |
| **DeepBrainz-R1** is our primary open research line. | |
| It is a family of reasoning-first SLMs designed for: | |
| - Multi-step reasoning | |
| - Long-context understanding | |
| - Research and agentic experimentation | |
| We publish multiple variants to support **transparency and reproducibility**. | |
| Only selected releases are considered **supported**. | |
| --- | |
| ## 🧱 Model Support Status | |
| - ✅ **Supported / Production** — curated, validated releases | |
| - 🧪 **Experimental** — exploratory variants | |
| - 🧱 **Research Checkpoints** — raw checkpoints for reproducibility | |
| - 👥 **Community Maintained** — third-party quantizations (GGUF, low-bit) | |
| --- | |
| ## Open Research | |
| DeepBrainz AI & Labs is an independent research lab. | |
| Our work is public, iterative, and driven by first-principles experimentation. | |
| Follow the organization to track ongoing releases and research updates. |