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 — Flagship production model Best starting point for reliable agentic systems.
- DeepBrainz-R1-2B — Balanced production model Strong reasoning with lower cost and latency.
- DeepBrainz-R1-0.6B-v2 — Canonical small model Cost-efficient baseline for small-model agent workloads.
- Long-context variants (16K / 40K) — early and experimental
- Research checkpoints — raw artifacts for ablation and evaluation
- Community quantizations (GGUF, 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
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.
Advancing General Intelligence through Scalable Reasoning