---
license: apache-2.0
datasets:
- vanta-research/human-ai-collaboration-2
language:
- en
base_model:
- mistralai/Ministral-3-8B-Reasoning-2512
base_model_relation: finetune
library_name: peft
tags:
- text-generation-inference
- logical-reasoning
- chat
- text
- conversational-ai
- vanta-research
- core-reasoning
- cognitive-architecture
- persona
- reasoning
- LLM
- vanta-research
- apollo-astralis
- large-language-model
- collaborative-ai
- conversational
- lora
- mistral
- ministral
- apache
- roleplay
- research
pipeline_tag: text-generation
---

<div align="center">

![vanta_trimmed](https://cdn-uploads.huggingface.co/production/uploads/686c460ba3fc457ad14ab6f8/hcGtMtCIizEZG_OuCvfac.png)
  
  <h1>VANTA Research</h1>
    
  <p><strong>Independent AI research lab building safe, resilient language models optimized for human-AI collaboration</strong></p>
  
  <p>
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  </p>
</div>

---

## Apollo Astralis 2

Apollo Astralis 2 is a fine-tuned language model built on the new Ministral 3 8B Reasoning architecture, optimized for:

- **Logical reasoning and inference**
- **Scientific and mathematical problem-solving**
- **Commonsense understanding**
- **Multi-step analytical thinking**
- **Collaborative problem-solving**

This model represents a 10% performance improvement over it's previous iteration, with significant gains across reasoning benchmarks while maintaining strong general capabilities.

---

### Model Details

- **Model Name**: Apollo Astralis 2
- **Developer**: VANTA Research
- **Base Model**: Ministral-3-8B-Reasoning-2512
- **Training Method**: Low-Rank Adaptation (LoRA)
- **Parameters**: 8B base + 70.5MB LoRA adapter
- **Training Data**:  Custom in-house synthetic data generation containing ~26,000 examples across reasoning, logic, math, and science domains

### Dataset Composition

- **Logical Reasoning** 
- **PIQA** 
- **Mathematics** 
- **Science & Commonsense** 
- **CommonsenseQA** 
- **WinoGrande** 
- **Human-AI Collaboration** 
- **Identity & Persona** 

---

## Benchmark Results

| Benchmark | Apollo Astralis 1 | Apollo Astralis 2 | Δ |
|-----------|-------------------|-------------------|---|
| **PIQA** | 90.0% | 90.0% | — |
| **WinoGrande** | 30.0% | **40.0%** | **+10.0%** |
| **CommonsenseQA** | 50.0% | **70.0%** | **+20.0%** |
| **Average** | 56.7% | **66.7%** | **+10.0%** |

---

## Quick Start

```python
import torch
from transformers import AutoTokenizer, BitsAndBytesConfig, Mistral3ForConditionalGeneration
# Note: PEFT not needed - this is the full merged model!

# Configure 4-bit quantization
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True,
)

# Load model
base_model = Mistral3ForConditionalGeneration.from_pretrained(
    "Ministral-3-8B-Reasoning-2512",
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True,
    torch_dtype=torch.float16,
)

model = Mistral3ForConditionalGeneration.from_pretrained(
    "vanta-research/apollo-astralis-2",
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True,
    torch_dtype=torch.float16,
)
tokenizer = AutoTokenizer.from_pretrained("vanta-research/apollo-astralis-2")
model.eval()
```
## Examples

### Logical Reasoning

```python
prompt = """If all roses are flowers, and some flowers fade quickly, can we conclude that some roses fade quickly? Explain your reasoning."""

# Apollo's response includes:
# - Clear problem breakdown
# - Syllogistic structure analysis
# - Identification of logical fallacy
# - Final conclusion with explanation
```

### Mathematical Problem Solving

```python
prompt = """A store offers 25% off, then an additional 10% off the sale price. Is this the same as 35% off? Show your work."""

# Apollo's response includes:
# - Step-by-step calculation
# - Comparison of compound vs simple discounts
# - Clear final answer
# - Practical explanation of why they differ
```

### Creative Problem Solving

```python
prompt = """I have a 3-liter jug and a 5-liter jug. How can I measure exactly 4 liters?"""

# Apollo's response includes:
# - Systematic approach
# - Step-by-step solution
# - Explanation of mathematical principles
# - Enthusiastic encouragement
```
---
### Technical Limitations

- **Memory**: Requires ~16GB for full precision inference (less with quantization)
- **Speed**: Response generation may be slower due to chain-of-thought reasoning
- **Deployment**: Best served via Ollama or HuggingFace; other formats may require conversion

## Ethical Considerations

### Responsible Use

- **Educational Focus**: Designed for learning and exploration, not professional advice
- **Verification Required**: Always verify critical information, especially in technical domains
- **Personality Awareness**: Warm tone should not be mistaken for emotional capacity or consciousness
- **Bias Acknowledgment**: May reflect biases from base model and training data

### Intended Use Cases

 **Appropriate**:
- Educational tutoring and homework help
- Learning reasoning and problem-solving skills
- Brainstorming and collaborative thinking
- Prototyping and development assistance
- Research into AI reasoning and persona stability

 **Inappropriate**:
- Professional legal, medical, or financial advice
- Critical decision-making without human oversight
- High-stakes applications without verification
- Contexts requiring formal, clinical communication

## Citation

```bibtex
@misc{apollo-astralis-2,
  title={Apollo Astralis 2},
  author={VANTA Research},
  year={2025},
  url={https://huggingface.co/vanta-research/apollo-astralis-2},
}
```

## License

Apache 2.0

---

## Contact
- Organization: hello@vantaresearch.xyz
- Engineering/Design: tyler@vantaresearch.xyz

*Proudly developed by VANTA Research in Portland, Oregon*