---
license: apache-2.0
language:
  - en
pipeline_tag: text-generation
tags:
  - deepbrainz
  - reasoning
  - mathematics
  - code
  - enterprise
  - 2b
  - long-context
library_name: transformers
---

### 🚀 Introducing DeepBrainz-R1 — Reasoning-First Small Language Models for Agentic Systems

Today we’re releasing **DeepBrainz-R1**, a family of **reasoning-first Small Language Models (SLMs)** designed for **agentic AI systems in real-world production**.

Agentic systems don’t ask once — they reason repeatedly. Tool calls, verification loops, schema-constrained outputs, retries, and long-context planning fundamentally change the economics and reliability requirements of language models. LLM-only stacks struggle under this load.

DeepBrainz-R1 is built from the opposite premise:

> **Reasoning is a trained behavior, not an emergent side-effect of scale.**

#### What DeepBrainz-R1 is designed for

* **Repeatable multi-step reasoning**, not one-shot chat
* **Agent-compatible behavior**: tool use, structured outputs, low-variance reasoning
* **Production economics**: lower latency, predictable cost, deployability
* **Inference-time scalability**: compute where needed, not everywhere

#### The R1 lineup

* **[DeepBrainz-R1-4B](https://huggingface.co/DeepBrainz/DeepBrainz-R1-4B)** — *Flagship production model*
  Best starting point for reliable agentic systems.
* **[DeepBrainz-R1-2B](https://huggingface.co/DeepBrainz/DeepBrainz-R1-2B)** — *Balanced production model*
  Strong reasoning with lower cost and latency.
* **[DeepBrainz-R1-0.6B-v2](https://huggingface.co/DeepBrainz/DeepBrainz-R1-0.6B-v2)** — *Canonical small model*
  Cost-efficient baseline for small-model agent workloads.
* **[Long-context variants (16K / 40K)](https://huggingface.co/collections/DeepBrainz/deepbrainz-r1-reasoning-first-slms-for-agentic-systems)** — early and experimental
* **[Research checkpoints](https://huggingface.co/collections/DeepBrainz/deepbrainz-r1-research-checkpoints)** — raw artifacts for ablation and evaluation
* **[Community quantizations (GGUF, low-bit)](https://huggingface.co/collections/DeepBrainz/deepbrainz-r1-community-quantizations-gguf-and-low-bit)** — community-maintained, not officially supported

We publish **supported releases, experimental variants, and research checkpoints separately** to keep expectations clear for builders, enterprises, and researchers.

#### Why now

2026 is the year agentic AI stops being a demo and starts becoming infrastructure. Infrastructure cannot rely on LLM-only economics or LLM-only reliability.
**Reasoning-first SLMs are the only viable path to scaling agents sustainably.**

— **DeepBrainz AI & Labs**

---

# DeepBrainz-R1-2B

**DeepBrainz-R1-2B** is a compact, high-performance reasoning model engineered by **DeepBrainz AI & Labs**. It is part of the **DeepBrainz-R1 Series**, designed to deliver frontier-class reasoning capabilities in cost-effective parameter sizes.

This variant features a **32,768 token context window**, optimized for processing medium-to-long documents and codebases.

---

## 🚀 Model Highlights

- **Parameter Count:** ~2B
- **Context Window:** 32,768 tokens
- **Specialization:** STEM Reasoning, Logic, Code Analysis
- **Architecture:** Optimized Dense Transformer
- **Deployment:** Ready for vLLM, SGLang, and local inference

---

## 🎯 Intended Use Cases

- **Agentic Workflows:** Reliability in multi-step planning tasks.
- **Math & Science:** Solving complex word problems and equations.
- **Code Generation:** Writing and debugging algorithms.
- **Structured Data Extraction:** Parsing and reasoning over unstructured text.

> **Note:** This model is post-trained for reasoning and agentic reliability.  
> For conversational chat, additional instruction tuning is recommended.

---

## 💻 Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "DeepBrainz/DeepBrainz-R1-2B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="bfloat16",
    device_map="auto"
)

prompt = "Analyze the time complexity of the following algorithm:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

---

## 🏗️ Technical Summary

The model has undergone **post-training** to enhance reasoning quality, stability, and agentic reliability.

*Detailed post-training recipes and dataset compositions are not fully disclosed.*

---

## 📜 License

This model is released under the **Apache 2.0** license, allowing for academic and commercial use.

---

<div align="center">
  <b>DeepBrainz AI & Labs</b><br>
  <i>Advancing General Intelligence through Scalable Reasoning</i>
</div>