Update README.md

README.md

@@ -53,23 +53,28 @@ Alpie-Core is one of the world's first fine-tuned 4-bit reasoning models, provin
 
 ## 4. Key Highlights
 
-- **Frontier Performance in 4-bit**: 81.28% MMLU, 92.75% GSM8K, 57.8% SWE-Bench Verified
-- **Global Ranking**: 3rd place on Humanity's Last Exam leaderboard
-- **Cost Advantage**: 70-88% lower inference cost vs GPT-4/Claude/DeepSeek
-- **Environmental Impact**: 64% lower carbon footprint per inference
-- **STEM + Coding Excellence**: Outperforms full-precision peers in mathematics and programming
-- **Enhanced Content Access**: Provides factual responses to geopolitically sensitive topics
+1. **Frontier Performance in 4-bit**: 81.28% MMLU, 92.75% GSM8K, 57.8% SWE-Bench Verified
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+2) **STEM + Coding Excellence**: Outperforms full-precision peers in mathematics and programming
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+3) **Enhanced Content Access**: Provides factual responses to geopolitically sensitive topics
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+4) **Quantization Efficiency**: A 4-bit quantized variant achieves competitive performance retention compared to full-precision models, demonstrating that aggressive quantization can preserve task accuracy while substantially reducing hardware requirements.
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+5) **Benchmark Competitiveness**: Across more than ten standard evaluation benchmarks, the model demonstrates performance on par with or exceeding that of larger 70B+ parameter systems, highlighting the effectiveness of our training and optimization strategies.
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+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
 
 | 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 |
 |-----------|----------------------|-------------------|-------------|---------------|---------------|----------------|----------------------------|
 | MMLU (5-shot) | **81.28%** | 78.4% | 85.0% | 84.4% | 79.3% | 78.6% | 80.73% |
-| GSM8K (8-shot) | **92.75%** | 81.6% | 88.3% | 83.5% | nan | 82.2% | 80.73% |
-| BBH (3-shot) | **85.12%** | 78.8% | 79.8% | 82.9% | 81.6% | 77.7% | nan |
+| GSM8K (8-shot) | **92.75%** | 81.6% | 88.3% | 83.5% | - | 82.2% | 80.73% |
+| BBH (3-shot) | **85.12%** | 78.8% | 79.8% | 82.9% | 81.6% | 77.7% | - |
 | MMLU-Pro (5-shot) | **64.78%** | 51.4% | 58.3% | 52.8% | 53.8% | 52.2% | 54.37% |
-| MBPP (pass@1) | **75.20%** | 65.0% | 72.6% | 68.4% | nan | 65.6% | 69.64% |
-| HumanEval (pass@1) | **57.23%** | 43.3% | 53.0% | 54.9% | nan | 48.8% | nan |
+| MBPP (pass@1) | **75.20%** | 65.0% | 72.6% | 68.4% | - | 65.6% | 69.64% |
+| HumanEval (pass@1) | **57.23%** | 43.3% | 53.0% | 54.9% | - | 48.8% | = |
 
 ### SWE-Bench Verified Performance
 
@@ -128,7 +133,15 @@ Alpie-Core is one of the world's first fine-tuned 4-bit reasoning models, provin
 
 ## 8. Use Cases
 
-Best for **STEM, complex mathematical reasoning, coding, and Indian context**.
+Best for **STEM**, **complex mathematical reasoning**, **coding**, and **Indian context**
+
+1)**STEM**: Excels at solving advanced problems in science, technology, engineering, and mathematics with high accuracy.
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+2)**Complex Mathematical Reasoning**: Handles multi-step logical and quantitative reasoning tasks with strong reliability.
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+3)**Coding**: Supports software development, debugging, and algorithmic problem-solving across multiple programming languages.
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+4)**Indian Context**: Provides culturally aware insights, competitive exam assistance (JEE, NEET, UPSC), and multilingual support in Hindi/Hinglish.
 
 
 ## 9. Safety and Limitations