tags:
- text-generation
- reasoning
- coding
- mathematics
- quantization
license: apache-2.0
datasets:
- synthetic
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
language:
- en
- hi
library_name: transformers
pipeline_tag: text-generation
Alpie-Core: 4-bit Quantized Reasoning Model
📄 Technical Report: Alpie_Core.pdf
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: 65k tokens
- Max Output Length: 16,384 tokens
- 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, optimized with high-quality LLM-generated responses. The fine-tuning process emphasized adherence to rigorous safety and usability standards, including:
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.
3)Limitations, Disclaimers, and Knowledge Boundaries – transparently communicating uncertainty and scope.
4)Handling Complex and Sensitive Topics – balancing informativeness with responsible guardrails.
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.
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
- Supports Streaming – Real-time token-level responses
- OpenAI-Compatible API – Seamless integration with OpenAI client libraries
- 65K Context Length – Handles very large inputs and conversations
- 16,384 Max Output Length – Enables extremely long generations
- 4-Bit Quantization – Memory-efficient and optimized for deployment
- High Throughput Inference – Powered by vLLM for efficient large-scale serving
- Low Latency Inference – Fast response times optimized for production
- Customizable Safety & Moderation Filters – Built-in guardrails for safer outputs
- Supports Function Calling / Tool Use – Enables structured outputs and external API integration
5. Key Highlights
- Frontier Performance in 4-bit: 81.28% MMLU, 92.75% GSM8K, 57.8% SWE-Bench Verified
STEM + Coding Excellence: Outperforms full-precision peers in mathematics and programming
Enhanced Content Access: Provides factual responses to geopolitically sensitive topics
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.
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.
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.
6. Benchmark Results
_-_Accuracy_Comparison.png)
| 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% | - | 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% | - | 65.6% | 69.64% |
| HumanEval (pass@1) | 57.23% | 43.3% | 53.0% | 54.9% | - | 48.8% | = |
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 |
| MATH-500 | 70.00% | Advanced Mathematics |
7. Training Details
- Hardware: 8× NVIDIA HOPPER-80GB GPUs
- Fine-tuning Method: LoRA/QLoRA with the following configuration:
- LoRA Alpha: 16
- LoRA Dropout: 0.05
- LoRA Rank: 16
- 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
8. Environmental Impact
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
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)
9. Use Cases
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.
3)Coding: Supports software development, debugging, and algorithmic problem-solving across multiple programming languages.
4)Indian Context: Provides culturally aware insights, competitive exam assistance (JEE, NEET, UPSC), and multilingual support in Hindi/Hinglish.
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.
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
11. How to Use
Non-Streaming Inference
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-4-bit"
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
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-4-bit"
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
12. Citation
@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}
}
13. License
Apache 2.0 – Free for research and commercial use
14. 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.
15. Contact
For technical inquiries and support: [email protected]
For technical details, training methodology, and comprehensive evaluation results, please refer to our technical report.