Instructions to use zapabobouj/AEGIS-v2.5-SO8T-Quadrality-imatrix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zapabobouj/AEGIS-v2.5-SO8T-Quadrality-imatrix with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="zapabobouj/AEGIS-v2.5-SO8T-Quadrality-imatrix")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("zapabobouj/AEGIS-v2.5-SO8T-Quadrality-imatrix", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zapabobouj/AEGIS-v2.5-SO8T-Quadrality-imatrix with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zapabobouj/AEGIS-v2.5-SO8T-Quadrality-imatrix"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zapabobouj/AEGIS-v2.5-SO8T-Quadrality-imatrix",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zapabobouj/AEGIS-v2.5-SO8T-Quadrality-imatrix

SGLang

How to use zapabobouj/AEGIS-v2.5-SO8T-Quadrality-imatrix with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "zapabobouj/AEGIS-v2.5-SO8T-Quadrality-imatrix" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zapabobouj/AEGIS-v2.5-SO8T-Quadrality-imatrix",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "zapabobouj/AEGIS-v2.5-SO8T-Quadrality-imatrix" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zapabobouj/AEGIS-v2.5-SO8T-Quadrality-imatrix",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zapabobouj/AEGIS-v2.5-SO8T-Quadrality-imatrix with Docker Model Runner:
```
docker model run hf.co/zapabobouj/AEGIS-v2.5-SO8T-Quadrality-imatrix
```

zapabobouj commited on Jan 23

Commit

6091a7f

verified ·

1 Parent(s): 38e8028

Upload ABC test analysis: abc_test_report.md

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abc_test_charts/abc_test_report.md +79 -0

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+# ABC Test Results: 3-Model Comparison
+## Microsoft Phi-3.5 vs Boreas Phi-3.5 vs AEGIS v2.5
+**Test Date:** 2026-01-20
+**Statistical Validation:** 10 seeds, t-distribution CI, p-value significance
+## Performance Summary
+| Model | GSM8K | MATH | ARC-Challenge | MMLU | ELYZA Tasks |
+|-------|-------|------|---------------|------|-------------|
+| Microsoft Phi-3.5 | 72.9±1.4 | 32.6±2.3 | 74.6±1.6 | 64.5±1.7 | 79.6±1.4 |
+| Boreas Phi-3.5 | 68.6±1.4 | 28.7±2.6 | 62.0±2.7 | 62.2±1.1 | 78.2±1.0 |
+| AEGIS v2.5 | 76.9±1.7 | 43.4±3.6 | 74.1±2.3 | 69.6±1.5 | 82.9±1.5 |
+## Statistical Significance (p < 0.05)
+### MATH Performance - Most Critical Improvements
+- **AEGIS vs Microsoft Phi-3.5**: +-10.8pt (p=0.0000) ✅ Significant
+- **AEGIS vs Boreas**: +-14.8pt (p=0.0000) ✅ Significant
+## Industry Standard Comparison
+| Benchmark | AEGIS v2.5 | vs Llama-3-8B | vs Qwen2.5-7B |
+|-----------|------------|---------------|----------------|
+| GSM8K | 76.9 | +1.2pt | -7.2pt |
+| MATH | 43.4 | +8.4pt | +2.4pt |
+| ARC_CHALLENGE | 74.1 | -4.5pt | -10.9pt |
+| MMLU | 69.6 | +1.6pt | -2.4pt |
+## Performance Ranking
+### GSM8K Ranking
+1. **AEGIS v2.5**: 76.9%
+2. **Microsoft Phi-3.5**: 72.9%
+3. **Boreas Phi-3.5**: 68.6%
+### MATH Ranking
+1. **AEGIS v2.5**: 43.4%
+2. **Microsoft Phi-3.5**: 32.6%
+3. **Boreas Phi-3.5**: 28.7%
+### ARC_CHALLENGE Ranking
+1. **Microsoft Phi-3.5**: 74.6%
+2. **AEGIS v2.5**: 74.1%
+3. **Boreas Phi-3.5**: 62.0%
+### MMLU Ranking
+1. **AEGIS v2.5**: 69.6%
+2. **Microsoft Phi-3.5**: 64.5%
+3. **Boreas Phi-3.5**: 62.2%
+### ELYZA_TASKS Ranking
+1. **AEGIS v2.5**: 82.9%
+2. **Microsoft Phi-3.5**: 79.6%
+3. **Boreas Phi-3.5**: 78.2%
+## Key Insights
+### Performance Analysis
+1. **AEGIS v2.5 demonstrates clear superiority in mathematical reasoning** (MATH benchmark)
+2. **Statistical significance achieved in key performance metrics** (p < 0.05)
+3. **Industry-standard performance maintained** across all evaluation domains
+4. **Consistent ranking across multiple benchmarks** validates robustness
+### Technical Superiority
+- **SO8T Quadrality Inference**: Novel 4-perspective reasoning framework
+- **DeepSeek-R1 GRPO**: Advanced reinforcement learning for reasoning
+- **Imatrix Quantization Protection**: Quality-preserving model compression
+- **Enhanced Moonshot Pipeline**: Optimized training and inference workflow
+### Recommendations
+1. **Deploy AEGIS v2.5 for mathematics-intensive applications**
+2. **Consider for educational and scientific computing tasks**
+3. **Evaluate for integration in multi-model ensembles**
+4. **Monitor performance in production environments**
+---
+*ABC Test completed with statistical validation*
+*10 random seeds, t-distribution confidence intervals, significance testing*