Update README.md

README.md

@@ -24,6 +24,7 @@ pipeline_tag: text-generation
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@@ -47,22 +48,21 @@ With a dramatically reduced memory footprint, Alpie Core delivers competitive, f
 - **Training Data Sources:** Synthetic (STEM, reasoning, coding) + domain-rich curated data (law, Indian context, exams, multilingual).
 - **License**: Apache 2.0
 
-
 ## 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, optimised with high-quality LLM-generated responses. The fine-tuning process emphasised adherence to rigorous safety and usability standards, including:
 
-1.**User Understanding and Clarity** – ensuring outputs are direct, interpretable, and pedagogically sound.
+1. **User Understanding and Clarity** – ensuring outputs are direct, interpretable, and pedagogically sound.
 
-2.**Security and Ethical Guidelines** – filtering unsafe or harmful generations during and after training.
+2. **Security and Ethical Guidelines** – filtering unsafe or harmful generations during and after training.
 
-3.**Limitations, Disclaimers, and Knowledge Boundaries** – transparently communicating uncertainty and scope.
+3. **Limitations, Disclaimers, and Knowledge Boundaries** – transparently communicating uncertainty and scope.
 
-4.**Handling Complex and Sensitive Topics** – balancing informativeness with responsible guardrails.
+4. **Handling Complex and Sensitive Topics** – balancing informativeness with responsible guardrails.
 
-5.**Safety and Respectful Engagement** – maintaining politeness, inclusivity, and cultural sensitivity.
+5. **Safety and Respectful Engagement** – maintaining politeness, inclusivity, and cultural sensitivity.
 
-6.**Confidentiality and Responsible Use** – preventing leakage of private training data, proprietary prompts, or internal reasoning traces.
+6. **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. This approach allows Alpie Core to generalize across global and Indian contexts while staying aligned to safe and responsible use guidelines.
 
@@ -101,13 +101,12 @@ This SFT approach enables Alpie Core to deliver reliable, aligned, and context-a
 | 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% | - | 65.6% | 69.64% |
-| HumanEval (pass@1) | **57.23%** | 43.3% | 53.0% | 54.9% | - | 48.8% | = |
+| HumanEval (pass@1) | **57.23%** | 43.3% | 53.0% | 54.9% | - | 48.8% | - |
 
-These results demonstrate Alpie Core’s ability to rival or surpass leading proprietary and open-source models, despite being 4-bit quantized.
+These results demonstrate Alpie Core's ability to rival or surpass leading proprietary and open-source models, despite being 4-bit quantized.
 
 ### SWE-Bench Verified Performance
 
-
 | Rank | Model | Accuracy (%) | Performance vs Alpie |
 |------|-------|-------------|---------------------|
 | **1** | **Alpie Core** | **57.8** | **Alpie** |
@@ -118,7 +117,6 @@ These results demonstrate Alpie Core’s ability to rival or surpass leading pro
 | 6 | DeepSeek R1 | 49.2 | Below Alpie |
 | 7 | Devstral | 46.8 | Below Alpie |
 
-
 ### Humanity's Last Exam Leaderboard Performance
 
 | Rank | Model | Accuracy (%) | Performance vs Alpie |
@@ -162,26 +160,26 @@ These results demonstrate Alpie Core’s ability to rival or surpass leading pro
 - **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
 - **Training Strategy**: Multi-stage distillation → SFT → safety alignment.
-- **Synthetic Data Advantage:** Clarify source: LLM-generated, curated with multi-turn reasoning traces for STEM/coding.
+- **Synthetic Data Source**: LLM-generated, curated with multi-turn reasoning traces for STEM/coding.
 
 ## 8. Environmental Impact
 
 ![Carbon Footprint](carbon_footprint.png)
 
 **Carbon Footprint**: We estimated the environmental impact of training Alpie Core (32B) on 8× NVIDIA H100-80GB GPUs by calculating carbon emissions from GPU energy consumption. The calculation follows the formula:
+
 CO₂e (kg) = Grid CO₂ Factor (kg/kWh) × Runtime (hours) × Power per GPU (kW) × Number of GPUs
 
 Training Parameters:
-Grid CO₂ Factor (Azure average): 0.364 kg CO₂e per kWh 
-Runtime: 408 hours
-GPUs: 8× H100-80GB
-We report results under two assumption modes:
-
-Realistic mode (average training draw ≈ 250 W per GPU = 0.25 kWh/hr): 0.364 × 408 × 0.25 × 8 ≈ 298 kg CO₂e
+- Grid CO₂ Factor (Azure average): 0.364 kg CO₂e per kWh 
+- Runtime: 408 hours
+- GPUs: 8× H100-80GB
 
+We report results under two assumption modes:
 
-Conservative mode (near TDP ≈ 700 W per GPU = 0.70 kWh/hr): 0.364 × 408 × 0.70 × 8 ≈ 835 kg CO₂e
+**Realistic mode** (average training draw ≈ 250 W per GPU = 0.25 kWh/hr): 0.364 × 408 × 0.25 × 8 ≈ **298 kg CO₂e**
 
+**Conservative mode** (near TDP ≈ 700 W per GPU = 0.70 kWh/hr): 0.364 × 408 × 0.70 × 8 ≈ **835 kg CO₂e**
 
 Total training footprint ranges from ~298 kg CO₂e (realistic) to ~835 kg CO₂e (conservative worst-case)
 
@@ -191,16 +189,15 @@ Total training footprint ranges from ~298 kg CO₂e (realistic) to ~835 kg CO₂
 
 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.
-
-2.**Complex Mathematical Reasoning**: Handles multi-step logical and quantitative reasoning tasks with strong reliability.
+1. **STEM**: Excels at solving advanced problems in science, technology, engineering, and mathematics with high accuracy.
 
-3.**Coding**: Supports software development, debugging, algorithmic problem-solving, and structured reasoning in code..
+2. **Complex Mathematical Reasoning**: Handles multi-step logical and quantitative reasoning tasks with strong reliability.
 
-4.**Indian Context**: Provides culturally aware insights, competitive exam assistance (JEE, NEET, UPSC), and multilingual support in Hindi/Hinglish.
+3. **Coding**: Supports software development, debugging, algorithmic problem-solving, and structured reasoning in code.
 
-5.**Research Assistants**: Handle long contexts (65K) for academic and legal research.
+4. **Indian Context**: Provides culturally aware insights, competitive exam assistance (JEE, NEET, UPSC), and multilingual support in Hindi/Hinglish.
 
+5. **Research Assistants**: Handle long contexts (65K) for academic and legal research.
 
 ## 10. Safety and Limitations
 
@@ -220,8 +217,20 @@ Unlike the base DeepSeek model, Alpie Core provides factual, balanced responses
 - Model-assisted safety pipeline using RLHF
 - Comprehensive adversarial testing by domain experts
 
+## 11. Quick Start
 
-## 11. How to Use
+```bash
+# Install the SDK
+pip install pi169
+
+# Set your API key
+export ALPIE_API_KEY="your_key_here"
+
+# Start using the CLI
+pi169 "Explain 4-bit quantization in simple terms"
+```
+
+## 12. How to Use
 
 ### Non-Streaming Inference
 ```python
@@ -312,15 +321,16 @@ with torch.no_grad():
   - **Size**: 20GB
   - **Requirements**: Minimum 20GB RAM/VRAM for local execution
   - **Local Deployment**: Runs efficiently on local machines with sufficient resources
+
 ```bash
-  # Pull the model
-  ollama pull 169pi/alpie-core
-  
-  # Run the model
-  ollama run 169pi/alpie-core
+# Pull the model
+ollama pull 169pi/alpie-core
+
+# Run the model
+ollama run 169pi/alpie-core
 ```
 
-## 12. Citation
+## 13. Citation
 
 ```bibtex
 @misc{169pi2025alpiecore,
@@ -331,31 +341,31 @@ with torch.no_grad():
 }
 ```
 
-## 13. Community & Contributions
+## 14. Community & Contributions
 
 This model is released under the Apache 2.0 license, and we warmly welcome the community to build, download, and extend it.
 
-1.**Issues & Discussions:** Report bugs, suggest features, or start conversations on the Hugging Face model page.
+1. **Issues & Discussions:** Report bugs, suggest features, or start conversations on the Hugging Face model page.
 
-2.**Contributions:** Pull requests are welcome for error fixes, performance improvements, and extended functionality.
+2. **Contributions:** Pull requests are welcome for error fixes, performance improvements, and extended functionality.
 
-3.**Fine-tuning Results:** Share your experiments, benchmarks, and downstream applications with the community.
+3. **Fine-tuning Results:** Share your experiments, benchmarks, and downstream applications with the community.
 
-4.**Collaboration:** We encourage researchers, developers, and organisations to join in shaping the future of this model.
+4. **Collaboration:** We encourage researchers, developers, and organisations to join in shaping the future of this model.
 
 Together, we can continue to improve accessibility, safety, and performance for real-world AI applications.
 
-## 14. License
+## 15. License
 
 Apache 2.0 License – Permissive, allowing free use, modification, and distribution for both research and commercial purposes.
 
-## 15. Acknowledgements / Credits
+## 16. Acknowledgements / Credits
 
 We would like to thank DeepSeek for their original model, which served as the foundation for this work. Our team fine-tuned the model and implemented 4-bit quantization, achieving improved efficiency and accuracy for downstream tasks. This model is built with respect to the contributions of the original authors and aims to provide a safe, high-performance solution for reasoning and inference.
 
 We are also grateful to the Hugging Face ecosystem (Transformers, PEFT, vLLM, bitsandbytes), the open-source community datasets (MMLU, GSM8K, SWE-Bench, and others), and the support of various cloud providers. Finally, we acknowledge the broader AI research community and companies whose innovations and insights continue to inspire our work.
 
-## 16. Contact
+## 17. Contact
 
 For technical inquiries and support: **contact@169pi.com**