Release: 32K Context Variant

README.md

@@ -10,14 +10,16 @@ tags:
   - code
   - enterprise
   - 0.6b
+  - long-context
+base_model: Qwen/Qwen3-0.6B
 library_name: transformers
 ---
 
 # DeepBrainz-R1-0.6B
 
-**DeepBrainz-R1-0.6B** is a compact, high-performance reasoning model engineered by **DeepBrainz AI & Labs**. Designed for efficiency and scalability, it specializes in structured chain-of-thought reasoning, mathematical problem solving, and logical analysis.
+**DeepBrainz-R1-0.6B** 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 model is part of the **DeepBrainz-R1 Series**, built 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.
 
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@@ -26,7 +28,7 @@ This model is part of the **DeepBrainz-R1 Series**, built to deliver frontier-cl
 - **Parameter Count:** ~0.6B
 - **Context Window:** 32,768 tokens
 - **Specialization:** STEM Reasoning, Logic, Code Analysis
-- **Architecture:** Optimized Dense Transformer (Qwen2.5/3 Compatible)
+- **Architecture:** Optimized Dense Transformer
 - **Deployment:** Ready for vLLM, TGI, and local inference
 
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@@ -65,9 +67,11 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 
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-## 🛡️ Limitations & Safety
+## 🏗️ Technical Summary
 
-While this model demonstrates strong reasoning capabilities, it may still produce inaccurate information ("hallucinations"). Users should implement appropriate guardrails for production deployments.
+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.
+
+*Specific training methodologies and dataset compositions are proprietary.*
 
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