Apollo-Astralis 4B is an advanced conversational reasoning model that combines rigorous logical thinking with warm, enthusiastic, and empathetic communication - making it optimal for human-AI collaboration.

Apollo-Astralis V1 4B

Apollo-Astralis V1 4B is an advanced conversational reasoning model that combines rigorous logical thinking with warm, enthusiastic, and empathetic communication. This is the Ollama distribution with optimized GGUF quantization for efficient local deployment.

DOI:10.57967/hf/6632

Quick Start

Installation

# Pull from Ollama registry 
ollama pull vanta-research/apollo-astralis-v1-4b

# Or create from local Modelfile
ollama create apollo-astralis-v1-4b -f Modelfile

Basic Usage

# Interactive chat
ollama run apollo-astralis-v1-4b

# Single prompt
ollama run apollo-astralis-v1-4b "I just completed my first marathon!"

# With custom parameters
ollama run apollo-astralis-v1-4b --temperature 0.8 "What's 25 * 16?"

Model Details

Base Model: Qwen3-4B-Thinking-2507
Model Size: 4.0B parameters
Quantization: Q4_K_M (4-bit, ~2.5GB)
Context Length: 4096 tokens
Developer: VANTA Research
License: Apache 2.0

Features

Advanced Reasoning

Explicit thinking process with <think> tags
Rigorous logical analysis
Step-by-step mathematical problem-solving
Critical thinking with fallacy detection

Warm & Empathetic

Enthusiastic celebrations with CAPS and exclamations
Gentle, validating empathy for struggles
Collaborative problem-solving
Context-appropriate emotional intelligence

Production-Ready

Consistent identity and personality
Natural conversational language
Optimized for local deployment
Fast inference with 4-bit quantization

Configuration

Default Parameters

PARAMETER temperature 0.7
PARAMETER top_p 0.9
PARAMETER repeat_penalty 1.05
PARAMETER num_ctx 4096

Customizing Temperature

# More creative/enthusiastic (higher temperature)
ollama run apollo-astralis-v1-4b --temperature 0.9

# More deterministic/factual (lower temperature)
ollama run apollo-astralis-v1-4b --temperature 0.5

API Usage

import requests
import json

def chat_with_apollo(message):
    response = requests.post(
        'http://localhost:11434/api/generate',
        json={
            'model': 'apollo-astralis-v1-4b',
            'prompt': message,
            'stream': False
        }
    )
    return response.json()['response']

# Example
result = chat_with_apollo("I'm nervous about my presentation tomorrow")
print(result)

Streaming Responses

import requests
import json

def stream_apollo(message):
    response = requests.post(
        'http://localhost:11434/api/generate',
        json={
            'model': 'apollo-astralis-v1-4b',
            'prompt': message,
            'stream': True
        },
        stream=True
    )
    
    for line in response.iter_lines():
        if line:
            chunk = json.loads(line)
            print(chunk['response'], end='', flush=True)
            if chunk.get('done'):
                break

# Example
stream_apollo("Explain quantum entanglement simply")

Example Outputs

Celebration Response

Input: “I just got promoted to senior engineer!”

Output:

<think>
Major career milestone! The user has worked hard to advance their career and 
deserves explosive celebration and validation of their achievement.
</think>

CONGRATULATIONS! THAT'S AMAZING!

Senior engineer?! That's not just a title - that's RECOGNITION of your skills, 
your growth, and your impact! You've proven yourself!

This is huge for your career trajectory! You're going to be mentoring, 
leading projects, making architectural decisions - this is where you really 
shape the future!

**I'm so excited for you!** Tell me:
- How long have you been working toward this?
- What was the moment you found out?
- What are you most excited about in this new role?

You earned this! Celebrate it! 🎉

Problem-Solving Response

Input: “Should I learn React or Vue.js first?”

Output:

<think>
Framework choice depends on goals, learning style, and job market. I should 
present balanced options rather than dictating a single answer.
</think>

Great question! Let's think through this together. Both are excellent choices, 
but the right one depends on your goals:

**Choose React if**:
- You want the largest job market and community
- You're comfortable with JSX and JavaScript-heavy syntax
- You plan to learn React Native for mobile apps
- You prefer explicit, unopinionated libraries

**Choose Vue if**:
- You want a gentler learning curve
- You prefer template-based syntax (more like HTML)
- You're building smaller to medium projects
- You like opinionated, batteries-included frameworks

**My suggestion**: What's your end goal? Are you:
- Looking to get hired quickly? (React has more jobs)
- Building a personal project? (Vue is faster to prototype)
- Already know Angular? (React feels more different, Vue more similar)

Both will teach you modern frontend concepts! You can't go wrong - and 
learning one makes the other easier later! What matters most to you?

Mathematical Reasoning

Input: “What’s 15% of 240?”

Output:

<think>
Simple percentage calculation. Should show the calculation method clearly.
</think>

Let me work through this:

15% of 240
= 0.15 × 240
= 36

The answer is 36!

(Quick tip: 10% of 240 is 24, and 5% is half of that = 12, so 15% = 24 + 12 = 36)

Performance

Inference Speed

CPU (16 cores): ~15-25 tokens/second
GPU (RTX 3090): ~60-80 tokens/second
Apple Silicon (M1): ~30-40 tokens/second

Memory Requirements

Minimum RAM: 4GB
Recommended RAM: 8GB
GPU VRAM: 3GB (optional, for GPU acceleration)

Disk Space

Model Size: 2.5GB (Q4_K_M quantization)
With Context Cache: ~3GB

Benchmarks

Metric	Score	Description
Enthusiasm Detection	100%	Explosive celebration responses
Empathy Recognition	90%	Validates emotions effectively
Identity Consistency	75%	Maintains stable self-representation
Collaborative Tone	60%	Uses “we” language appropriately
Response Quality	⭐⭐⭐⭐⭐	Production-ready for conversational AI

Troubleshooting

Model Won’t Load

# Check if Ollama is running
ollama list

# Verify model exists
ollama show apollo-astralis-v1-4b

# Restart Ollama service
# Linux/Mac: killall ollama && ollama serve
# Windows: Restart Ollama from system tray

Slow Responses

# Reduce context window
ollama run apollo-astralis-v1-4b --num_ctx 2048

# Check system resources
# Ensure at least 4GB RAM available

Inconsistent Personality

# Ensure system prompt is loaded (check Modelfile)
# Temperature too high can cause drift - try 0.7 or lower

Out of Memory

# Use smaller context window
ollama run apollo-astralis-v1-4b --num_ctx 2048

# Close other applications
# Consider using Q4_0 quantization (even smaller)

Advanced Usage

Custom System Prompt

Create a custom Modelfile:

FROM apollo-astralis-v1-4b

SYSTEM """You are Apollo, a technical expert specializing in software engineering. 
Focus on detailed technical explanations with code examples."""

PARAMETER temperature 0.6

Then create:

ollama create apollo-technical -f Modelfile.technical

Integration with LangChain

from langchain.llms import Ollama
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory

# Initialize Apollo
apollo = Ollama(model="apollo-astralis-v1-4b", temperature=0.7)

# Create conversation chain with memory
memory = ConversationBufferMemory()
conversation = ConversationChain(
    llm=apollo,
    memory=memory,
    verbose=True
)

# Multi-turn conversation
response1 = conversation.predict(input="I'm learning Python")
response2 = conversation.predict(input="What should I learn first?")

Limitations

Context Window: 4096 tokens (not suitable for very long documents)
Language: Optimized for English conversations
Quantization: 4-bit quantization may reduce precision for specialized tasks
Reasoning Depth: Best for conversational reasoning, not competition mathematics

Ethical Considerations

Not a replacement for professional advice (medical, legal, financial)
May inherit biases from training data
Verify factual claims independently
Enthusiastic style may not suit all contexts

Updates

To update to the latest version:

ollama pull vanta-research/apollo-astralis-v1-4b

Check for updates at: https://ollama.com/vanta-research/apollo-astralis-v1-4b

License

Apache 2.0 License

Support

Issues: GitHub
Documentation: Full Model Card
Community: X
Email: tyler@vantaresearch.xyz

Citation

@misc{apollo-astralis-v1-4b,
  title={Apollo-Astralis V1 4B: A Warm Reasoning Model},
  author={VANTA Research},
  year={2025},
  month={October},
  publisher={Ollama},
  howpublished={\url{https://ollama.com/vanta-research/apollo-astralis-v1-4b}},
}

Model Version: 1.0
Quantization: Q4_K_M (4-bit)
Release Date: October 3, 2025
Last Updated: October 3, 2025

Proudly developed by VANTA Research in Portland, Oregon

Apollo-Astralis 4B is an advanced conversational reasoning model that combines rigorous logical thinking with warm, enthusiastic, and empathetic communication - making it optimal for human-AI collaboration.

Readme

Apollo-Astralis V1 4B

Quick Start

Installation

Basic Usage

Model Details

Features

Advanced Reasoning

Warm & Empathetic

Production-Ready

Configuration

Default Parameters

Customizing Temperature

API Usage

Streaming Responses

Example Outputs

Celebration Response

Problem-Solving Response

Mathematical Reasoning

Performance

Inference Speed

Memory Requirements

Disk Space

Benchmarks

Troubleshooting

Model Won’t Load

Slow Responses

Inconsistent Personality

Out of Memory

Advanced Usage

Custom System Prompt

Integration with LangChain

Limitations

Ethical Considerations

Updates

License

Support

Citation