Askit-OLMo-32B-Spatial-Thinking-Preview is not just another code generation model. It's a spatial reasoning specialist that thinks in 3D coordinates before writing code.

Key Features: ✅ Forced Spatial Cognition: Every problem decomposed into 3D coordinates ✅ API-Centric Reasoning: Physics principles → PhysicsBridge API calls ✅ Explicit Reasoning: Complete thinking chain visible in output ✅ Competition-Ready: Optimized for CPhO & IMO level problems

🛠️ Ecosystem & Integration

📱 Official Software

💻 GitHub: github.com/SwayingWheatfield/Askit.

🔗 Integration Points

Askit-OLMo-32B Model
        ↓
    Generates Code
        ↓
PhysicsBridge API
        ↓
Askit. Platform
        ↓
Real-time Visualization

📚 Related Projects

Project	Purpose	Link
Askit.	Interactive Animation Platform	GitHub
PhysicsBridge	Physics Engine Wrapper	Integrated in Askit.
OLMo-3.1-32B	Base Model	Allen AI

📊 Model Specifications

Aspect	Details
Base Model	OLMo-3.1-32B-Instruct
Fine-tuning	LoRA (Rank 256)
Training Data	3,500+ physics/math problems
Framework	DeepSpeed ZeRO-3 + BF16
Hardware	3x RTX 5090 GPUs
Output Format	Explicit reasoning chains + code

💡 Output Format

The model generates complete reasoning chains with explicit spatial thinking:

<thought>

3D space structure analysis
Initial positions (x₀, y₀, z₀)
Initial velocities (vₓ, vᵧ, vᵤ)
Coordinate system setup


Applicable physics laws
Force analysis
Acceleration calculations


Position at time t: (x(t), y(t), z(t))
Velocity vector: (vₓ(t), vᵧ(t), vᵤ(t))
Trajectory equations

PhysicsBridge API calls
Parameter mapping: coordinates → API
Initial conditions setup
</thought>

<code>
# PhysicsBridge API Integration
physics = PhysicsBridge()
physics.create_rigid_body(
    position=(x₀, y₀, z₀),
    velocity=(vₓ, vᵧ, vᵤ),
    mass=m,
    shape='sphere'
)
# ... more API calls
</code>

🚀 Quick Start

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "SStarrySSky/Askit-OLMo-32B-Spatial-Thinking-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

# Physics simulation with spatial reasoning
prompt = """
Create a physics simulation for a ball dropped from 10 meters.
Ball mass: 1kg, initial velocity: (0, 0, 0)
Use PhysicsBridge API.
"""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=2048, temperature=0.7)
print(tokenizer.decode(outputs[0]))

📖 Use Cases

🎓 Physics Education

Interactive animations for teaching concepts
Explicit spatial reasoning aids student understanding
API-driven code runs directly in Askit. platform

📊 Mathematical Visualization

Visual demonstrations of math problems
Geometric accuracy through coordinate calculations
Perfect for IMO-level problem visualization

🔬 Research Simulation

Academic research physics simulations
Correct coordinate systems guaranteed
Real-time rendering via PhysicsBridge

🏆 Competitive Problem Solving

CPhO and IMO level problem solving
Forced spatial reasoning matches competition requirements
Production-ready simulation code

🔗 Links & Resources

Official Channels

💻 GitHub Repository: github.com/SwayingWheatfield/Askit.
🤗 HuggingFace Model: SwayingWheatfield/Askit-OLMo-32B-Spatial-Thinking-Preview

Base Technologies

🧠 OLMo-3.1-32B: allenai/OLMo-3.1-32B-Instruct
⚙️ Transformers: huggingface.co/transformers

📄 License

GPL-3.0 License - See LICENSE

🙏 Acknowledgments

Built on top of OLMo-3.1-32B-Instruct by Allen Institute for AI.

Integrated with Askit. - Interactive Physics Animation Platform.

Made with ❤️ by Starry Sky

🌐 Website • 💻 GitHub • 🤗 HuggingFace

SwayingWheatfield/OLMo-32B-Spatial-Thinking-Preview

🚀 Askit-OLMo-32B-Spatial-Thinking-Preview

✨ What Makes Askit Special?