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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.*