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README.md

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+# Alpie-Core: 4-bit Quantized Reasoning Model
+
+---
+
+*[Space reserved for blog paper, technical report links, and company logo]*
+
+---
+
+## 1. Introduction
+
+Alpie-Core is one of the world's first fine-tuned 4-bit reasoning models, proving that aggressive quantization can surpass full-precision baselines in reasoning, mathematics, and coding. By combining cutting-edge quantization-aware training with synthetic STEM-rich datasets, Alpie-Core achieves frontier-level reasoning while being practical for real-world deployment at scale.
+
+## 2. Model Summary
+
+- **Base Architecture**: DeepSeek-R1-Distill-Qwen-32B
+- **Parameters**: 32 billion (quantized to 4-bit)
+- **Training Method**: Supervised Fine-Tuning (SFT) using LoRA/QLoRA techniques
+- **Quantization**: 4-bit NF4 with double quantization
+- **Context Length**: 65,536 tokens
+- **Max Output Length**: 16,384 tokens
+- **License**: Apache 2.0
+- **Memory Footprint**: ~8GB (75% reduction from full-precision)
+
+## 3. Model Features
+
+1. **Supports Streaming** – Real-time token-level responses
+2. **OpenAI-Compatible API** – Seamless integration with OpenAI client libraries
+3. **65K Context Length** – Handles very large inputs and conversations
+4. **16,384 Max Output Length** – Enables extremely long generations
+5. **4-Bit Quantization** – Memory-efficient and optimized for deployment
+6. **High Throughput Inference** – Powered by vLLM for efficient large-scale serving
+7. **Low Latency Inference** – Fast response times optimized for production
+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
+
+- **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
+
+## 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 |
+| 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 |
+
+### SWE-Bench Verified Performance
+
+| Rank | Model | Accuracy (%) | Performance vs Alpie |
+|------|-------|-------------|---------------------|
+| **1** | **Alpie Core** | **57.8** | **Alpie** |
+| 2 | Qwen3-Coder-30B-A3B-Instruct | 51.6 | Below Alpie |
+| 3 | o1 | 48.9 | Below Alpie |
+| 4 | o3-mini (high) | 49.3 | Below Alpie |
+| 5 | Claude 3.5 Sonnet | 49.0 | Below Alpie |
+| 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 |
+|------|-------|-------------|---------------------|
+| 1 | GPT 4.5 Preview | 5.8 | Above Alpie |
+| 2 | Claude Sonnet 4 | 5.42 | Above Alpie |
+| **3** | **Alpie Core 32B (4-bit)** | **5.41** | **Alpie** |
+| 4 | Llama 4 Maverik | 5.34 | Below Alpie |
+| 5 | GPT 4.1 | 4.97 | Below Alpie |
+| 6 | Kimi K2 Instruct | 4.68 | Below Alpie |
+| 7 | DeepSeek V3 | 4.55 | Below Alpie |
+| 8 | Gemini 1.5 Pro 002 | 4.55 | Below Alpie |
+
+### Additional Benchmarks
+
+| Benchmark | Alpie-Core (32B-4bit) | Category |
+|-----------|----------------------|----------|
+| AIME | **47.34%** | Advanced Mathematics |
+| GPQA (Diamond) | **40.91%** | Graduate-level QA |
+| TruthfulQA (MC2) | **60.05%** | Truthfulness |
+| HellaSwag | **84.66%** | Commonsense |
+| PIQA | **83.24%** | Physical Reasoning |
+| ARC Challenge | **67.58%** | Science QA |
+| CommonSenseQA | **87.06%** | Commonsense |
+| AGIEval | **64.98%** | General Intelligence |
+| Winogrande | **79.53%** | Commonsense Reasoning |
+
+## 6. Training Details
+
+- **Hardware**: 8× NVIDIA A100-80GB GPUs
+- **Training Duration**: 408 hours
+- **Fine-tuning Method**: LoRA/QLoRA with the following configuration:
+  - LoRA Alpha: 8
+  - LoRA Dropout: 0.05
+  - LoRA Rank: 8
+- **Quantization**: 4-bit NF4 + Double Quantization + FP16 compute
+- **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
+
+**Carbon Footprint**: 298-835 kg CO₂e (training)
+
+## 8. Use Cases
+
+### Scientific Research Excellence
+- 98% performance on SciQ benchmark
+- Advanced physics, chemistry, and mathematical sciences
+- Literature review automation and hypothesis generation
+- Experimental design optimization
+
+### Advanced Coding and Software Engineering
+- 57.8% SWE-Bench Verified score (8% above nearest competitor)
+- Automated bug detection and GitHub issue resolution
+- Competitive programming and algorithm design
+- Enterprise software development and architecture design
+
+### Indian Cultural and Religious Expertise
+- Comprehensive understanding of Hindu philosophy, Buddhist traditions
+- Regional diversity and cultural knowledge across Indian states
+- Legal and constitutional framework understanding
+- Educational support for Indian competitive exams (JEE, NEET, UPSC, SSC)
+
+## 9. 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.
+
+### Current Limitations
+- Multilingual reasoning in Hindi/Hinglish shows room for improvement
+- Fixed knowledge cutoff without real-time information retrieval
+- Occasional struggles with complex multi-hop mathematical reasoning
+- Potential hallucinations in factual question-answering
+
+### Mitigations
+- Safety classifiers and output filtering systems
+- Model-assisted safety pipeline using RLHF
+- Comprehensive adversarial testing by domain experts
+
+## 10. How to Use
+
+### Non-Streaming Inference
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel, PeftConfig
+import torch
+
+# Load LoRA adapter configuration to find the base model
+peft_model_id = "169Pi/Alpie-core"
+config = PeftConfig.from_pretrained(peft_model_id)
+
+# Load the base model
+base_model = AutoModelForCausalLM.from_pretrained(
+    config.base_model_name_or_path,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+
+# Load LoRA weights
+model = PeftModel.from_pretrained(base_model, peft_model_id)
+
+# Ensure evaluation mode
+model.eval()
+
+# Sample inference
+prompt = "Solve the Riemann Hypothesis and provide a final answer?"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    outputs = model.generate(**inputs, max_new_tokens=1000)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+print("Response:\n", response)
+```
+
+### Streaming Inference
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+from peft import PeftModel, PeftConfig
+import torch
+
+# Load LoRA adapter configuration to find the base model
+peft_model_id = "169Pi/Alpie-core"
+config = PeftConfig.from_pretrained(peft_model_id)
+
+# Load the base model
+base_model = AutoModelForCausalLM.from_pretrained(
+    config.base_model_name_or_path,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+
+# Load LoRA weights
+model = PeftModel.from_pretrained(base_model, peft_model_id)
+
+# Ensure evaluation mode
+model.eval()
+
+# Initialize streamer
+streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+# Sample streaming inference
+prompt = "Solve the Riemann Hypothesis and provide a final answer?"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+print("Streaming Response:")
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=1000,
+        streamer=streamer,
+        do_sample=True,
+        temperature=0.7,
+        top_p=0.9
+    )
+```
+
+### Deployment Options
+- **Transformers**: Python, PyTorch integration
+- **vLLM**: High-throughput inference
+- **LMDeploy/Ollama/TensorRT-LLM**: Production deployments
+
+## 11. Citation
+
+```bibtex
+@misc{alpie2025core,
+  title     = {Alpie-Core: A 4-bit Quantized Reasoning Model Surpassing Full-Precision Benchmarks},
+  author    = {Alpie AI},
+  year      = {2025},
+  url       = {https://huggingface.co/alpie/Alpie-Core-4bit}
+}
+```
+
+## 12. License
+
+Apache 2.0 – Free for research and commercial use
+
+---
+
+*For technical details, training methodology, and comprehensive evaluation results, please refer to our technical report.*