Update Model Card: Official Commercial Release

README.md

@@ -4,49 +4,50 @@ language:
   - en
 pipeline_tag: text-generation
 tags:
-  - qwen3
+  - deepbrainz
   - reasoning
-  - long-context
-  - distillation
-  - math
+  - mathematics
+  - code
   - enterprise
-  - research
+  - 4b
+  - long-context
 base_model: Qwen/Qwen3-4B
+library_name: transformers
 ---
 
 # DeepBrainz-R1-4B-16K
 
-**DeepBrainz-R1-4B-16K** is a high-performance reasoning model in the **DeepBrainz-R series**, designed for structured problem-solving, analysis, and enterprise research workflows. 
+**DeepBrainz-R1-4B-16K** is a compact, high-performance reasoning model engineered by **DeepBrainz AI & Labs**. Designed for scalability and efficiency, it specializes in structured chain-of-thought reasoning, mathematical problem solving, and logical analysis.
 
-It is distilled from the **Qwen3-32B** teacher model into a compact **4B** architecture using **Online Policy Distillation (OPD)**, emphasizing reasoning quality and instruction robustness over a **16K context window**.
+This model is part of the **DeepBrainz-R1 Series**, built to deliver frontier-class reasoning capabilities in cost-effective parameter sizes.
 
 ---
 
-## Model Highlights
+## 🚀 Model Highlights
 
-- **4B Parameters**: Optimized balance of performance and inference cost.
-- **16K Context Length**: Capable of processing medium-to-long documents and reasoning chains.
-- **Distilled Precision**: Trained via NeMo-RL OPD from a **Qwen3-32B** teacher.
-- **Architecture**: Standard Qwen3 (Dense), optimized for modern GPU inference.
+- **Parameter Count:** ~4B
+- **Context Window:** 16,384 tokens
+- **Specialization:** STEM Reasoning, Logic, Code Analysis
+- **Architecture:** Optimized Dense Transformer (Qwen2.5/3 Compatible)
+- **Deployment:** Ready for vLLM, TGI, and local inference
 
 ---
 
-## Intended Use
+## 🎯 Intended Use Cases
 
-- **Complex Reasoning**: Multi-step math, logic puzzles, and code analysis.
-- **Agentic Workflows**: Reliable planning and tool use within 16K context.
-- **Research**: Investigating distillation scaling laws (32B $\to$ 4B).
-- **Efficient Deployment**: Fits easily on consumer GPUs and edge servers.
+- **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 optimized for reasoning tasks. For general conversational chit-chat, we recommend applying a specific instruction template.*
+> **Note:** This is a base reasoning model. For conversational chat, we recommend using a specific instruct template or fine-tuning on your domain data.
 
 ---
 
-## Usage
+## 💻 Usage
 
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
 
 model_id = "DeepBrainz/DeepBrainz-R1-4B-16K"
 
@@ -57,46 +58,36 @@ model = AutoModelForCausalLM.from_pretrained(
     device_map="auto"
 )
 
-# Example: Math Reasoning
-prompt = "Solve step by step: If 3x + 7 = 22, what is x?"
+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=512,
-    temperature=0.6,
-    top_p=0.95,
-    do_sample=True
-)
-
+outputs = model.generate(**inputs, max_new_tokens=256)
 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
 
 ---
 
-## Training Summary
+## 🏗️ Technical Summary
 
-The model was produced using a **multi-stage optimization process** involving large-scale supervision and iterative refinement to improve reasoning quality and robustness. 
+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.
 
-- **Teacher**: Qwen3-32B (Dense)
-- **Student**: Qwen3-4B
-- **Method**: Online Policy Distillation (OPD)
-- **Context**: 16,384 tokens
+*Specific training methodologies and dataset compositions are proprietary.*
 
 ---
 
-## Limitations
+## 🛡️ Limitations & Safety
 
-Performance depends on task complexity and inference configuration. While significantly stronger than smaller models, it may still hallucinate on obscure facts compared to 30B+ models.
+While this model demonstrates strong reasoning capabilities, it may still produce inaccurate information ("hallucinations"). Users should implement appropriate guardrails for production deployments.
 
 ---
 
-## License
+## 📜 License
 
-Apache 2.0
+This model is released under the **Apache 2.0** license, allowing for academic and commercial use.
 
 ---
 
-## About DeepBrainz
-
-DeepBrainz builds reasoning-first AI systems focused on efficiency, structure, and real-world problem-solving.
+<div align="center">
+  <b>DeepBrainz AI & Labs</b><br>
+  <i>Advancing General Intelligence through Scalable Reasoning</i>
+</div>