Update README.md

README.md

@@ -60,7 +60,7 @@ We publish **supported releases, experimental variants, and research checkpoints
 
 **DeepBrainz-R1-4B** 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 specific variant offers a **32,768 token context window**, making it suitable for `extended context version optimized for medium-length document analysis.`.
+This variant offers an extended context window (up to 32,768 tokens), making it suitable for medium-length document and code analysis.
 
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@@ -82,7 +82,8 @@ This specific variant offers a **32,768 token context window**, making it suitab
 - **Code Generation:** Writing and debugging algorithms.
 - **Structured Data Extraction:** Parsing and reasoning over unstructured text.
 
-> **Note:** This is a base reasoning model. For conversational chat, we recommend using a specific instruct template or fine-tuning on your domain data.
+> **Note:** This model has undergone post-training to enhance reasoning quality and agentic reliability.  
+> It is not optimized for open-ended conversational chat without additional instruction tuning.
 
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@@ -111,9 +112,9 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 
 ## 🏗️ Technical Summary
 
-The model was produced using a **multi-stage optimization process** involving large-scale supervision and iterative refinement. It is designed to maximize reasoning quality while maintaining instruction robustness.
+The model has undergone **post-training** to improve reasoning quality, output stability, and robustness under agentic workloads.
 
-*Specific training methodologies and dataset compositions are proprietary.*
+*Detailed post-training recipes and dataset compositions are not fully disclosed.*
 
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