Update README.md

README.md

@@ -39,7 +39,25 @@ Alpie-Core is one of the world's first fine-tuned 4-bit reasoning models, provin
 - **License**: Apache 2.0
 
 
-## 3. Model Features
+## 3. Approach
+
+**Alpie-Core** has undergone extensive **supervised fine-tuning (SFT)** to strengthen reasoning, robustness, and safety. The training leveraged a diverse mixture of curated open-source datasets and proprietary synthetic data, optimized with high-quality LLM-generated responses. The fine-tuning process emphasized adherence to rigorous safety and usability standards, including:
+
+**User Understanding and Clarity** – ensuring outputs are direct, interpretable, and pedagogically sound.
+
+**Security and Ethical Guidelines** – filtering unsafe or harmful generations during and after training.
+
+**Limitations, Disclaimers, and Knowledge Boundaries** – transparently communicating uncertainty and scope.
+
+**Handling Complex and Sensitive Topics** – balancing informativeness with responsible guardrails.
+
+**Safety and Respectful Engagement** – maintaining politeness, inclusivity, and cultural sensitivity.
+
+**Confidentiality and Responsible Use** – preventing leakage of private training data, proprietary prompts, or internal reasoning traces.
+
+This SFT approach enables Alpie-Core to deliver reliable, aligned, and context-aware responses while maintaining safety across a broad range of use cases.
+
+## 4. Model Features
 
 1. **Supports Streaming** – Real-time token-level responses
 2. **OpenAI-Compatible API** – Seamless integration with OpenAI client libraries
@@ -51,7 +69,7 @@ Alpie-Core is one of the world's first fine-tuned 4-bit reasoning models, provin
 8. **Customizable Safety & Moderation Filters** – Built-in guardrails for safer outputs
 9. **Supports Function Calling / Tool Use** – Enables structured outputs and external API integration
 
-## 4. Key Highlights
+## 5. Key Highlights
 
 1. **Frontier Performance in 4-bit**: 81.28% MMLU, 92.75% GSM8K, 57.8% SWE-Bench Verified
 
@@ -65,7 +83,7 @@ Alpie-Core is one of the world's first fine-tuned 4-bit reasoning models, provin
 
 6) **Environmental Benefits**: Through quantization and efficiency-focused design, the model requires significantly fewer computational resources. This translates into lower energy consumption and reduced carbon footprint relative to full-precision deployments.
 
-## 5. Benchmark Results
+## 6. Benchmark Results
 
 | Benchmark | Alpie-Core (32B-4bit) | DeepSeek-V2 (236B) | Qwen2.5 72B | Llama 3.1 405B | Llama 3.1 70B | Gemma-3 27B-PT | Mistral-Small-24B-Base-2501 |
 |-----------|----------------------|-------------------|-------------|---------------|---------------|----------------|----------------------------|
@@ -115,7 +133,7 @@ Alpie-Core is one of the world's first fine-tuned 4-bit reasoning models, provin
 | AGIEval | **64.98%** | General Intelligence |
 | Winogrande | **79.53%** | Commonsense Reasoning |
 
-## 6. Training Details
+## 7. Training Details
 
 - **Hardware**: 8× NVIDIA H100-80GB GPUs
 - **Training Duration**: 408 hours
@@ -127,11 +145,11 @@ Alpie-Core is one of the world's first fine-tuned 4-bit reasoning models, provin
 - **Dataset Domains**: Mathematics, coding, reasoning, science, general knowledge, competitive exams, Indian context + law, multilingual (Hindi and Hinglish)
 - **Synthetic Data Advantage**: +15-20% performance boost in STEM & coding domains
 
-## 7. Environmental Impact
+## 8. Environmental Impact
 
 **Carbon Footprint**: 298-835 kg CO₂e (training)
 
-## 8. Use Cases
+## 9. Use Cases
 
 Best for **STEM**, **complex mathematical reasoning**, **coding**, and **Indian context**
 
@@ -144,7 +162,7 @@ Best for **STEM**, **complex mathematical reasoning**, **coding**, and **Indian
 4)**Indian Context**: Provides culturally aware insights, competitive exam assistance (JEE, NEET, UPSC), and multilingual support in Hindi/Hinglish.
 
 
-## 9. Safety and Limitations
+## 10. Safety and Limitations
 
 ### Enhanced Content Access
 Unlike the base DeepSeek model, Alpie-Core provides factual, balanced responses to geopolitically sensitive questions, offering global accessibility and factual accuracy on topics like Taiwan's status, Arunachal Pradesh sovereignty, and other sensitive geopolitical issues.
@@ -160,7 +178,7 @@ Unlike the base DeepSeek model, Alpie-Core provides factual, balanced responses
 - Model-assisted safety pipeline using RLHF
 - Comprehensive adversarial testing by domain experts
 
-## 10. How to Use
+## 11. How to Use
 
 ### Non-Streaming Inference
 ```python
@@ -249,7 +267,7 @@ with torch.no_grad():
 - **vLLM**: High-throughput inference
 - **LMDeploy/Ollama/TensorRT-LLM**: Production deployments
 
-## 11. Citation
+## 12. Citation
 
 ```bibtex
 @misc{alpie2025core,
@@ -260,7 +278,7 @@ with torch.no_grad():
 }
 ```
 
-## 12. License
+## 13. License
 
 Apache 2.0 – Free for research and commercial use