---
title: README
emoji: 🏃
colorFrom: green
colorTo: indigo
sdk: static
pinned: true
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.

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## 🔑 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**.

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### 🧠 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

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## 🧪 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.

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## 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

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## 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**.

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## 🧱 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)

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## 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.