Update README.md

README.md

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 library_name: transformers
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+### 🚀 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.
@@ -28,7 +68,7 @@ This variant features a **32,768 token context window**, optimized for processin
 - **Context Window:** 32,768 tokens
 - **Specialization:** STEM Reasoning, Logic, Code Analysis
 - **Architecture:** Optimized Dense Transformer
-- **Deployment:** Ready for vLLM, TGI, and local inference
+- **Deployment:** Ready for vLLM, SGLang, and local inference
 
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