Instructions to use SwayingWheatfield/OLMo-32B-Spatial-Thinking-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use SwayingWheatfield/OLMo-32B-Spatial-Thinking-Preview with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="SwayingWheatfield/OLMo-32B-Spatial-Thinking-Preview", filename="model.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use SwayingWheatfield/OLMo-32B-Spatial-Thinking-Preview with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf SwayingWheatfield/OLMo-32B-Spatial-Thinking-Preview # Run inference directly in the terminal: llama-cli -hf SwayingWheatfield/OLMo-32B-Spatial-Thinking-Preview
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf SwayingWheatfield/OLMo-32B-Spatial-Thinking-Preview # Run inference directly in the terminal: llama-cli -hf SwayingWheatfield/OLMo-32B-Spatial-Thinking-Preview
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf SwayingWheatfield/OLMo-32B-Spatial-Thinking-Preview # Run inference directly in the terminal: ./llama-cli -hf SwayingWheatfield/OLMo-32B-Spatial-Thinking-Preview
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf SwayingWheatfield/OLMo-32B-Spatial-Thinking-Preview # Run inference directly in the terminal: ./build/bin/llama-cli -hf SwayingWheatfield/OLMo-32B-Spatial-Thinking-Preview
Use Docker
docker model run hf.co/SwayingWheatfield/OLMo-32B-Spatial-Thinking-Preview
- LM Studio
- Jan
- Ollama
How to use SwayingWheatfield/OLMo-32B-Spatial-Thinking-Preview with Ollama:
ollama run hf.co/SwayingWheatfield/OLMo-32B-Spatial-Thinking-Preview
- Unsloth Studio new
How to use SwayingWheatfield/OLMo-32B-Spatial-Thinking-Preview with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for SwayingWheatfield/OLMo-32B-Spatial-Thinking-Preview to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for SwayingWheatfield/OLMo-32B-Spatial-Thinking-Preview to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for SwayingWheatfield/OLMo-32B-Spatial-Thinking-Preview to start chatting
- Docker Model Runner
How to use SwayingWheatfield/OLMo-32B-Spatial-Thinking-Preview with Docker Model Runner:
docker model run hf.co/SwayingWheatfield/OLMo-32B-Spatial-Thinking-Preview
- Lemonade
How to use SwayingWheatfield/OLMo-32B-Spatial-Thinking-Preview with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull SwayingWheatfield/OLMo-32B-Spatial-Thinking-Preview
Run and chat with the model
lemonade run user.OLMo-32B-Spatial-Thinking-Preview-{{QUANT_TAG}}List all available models
lemonade list
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SwayingWheatfield/OLMo-32B-Spatial-Thinking-Preview# Run inference directly in the terminal:
llama-cli -hf SwayingWheatfield/OLMo-32B-Spatial-Thinking-PreviewUse pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf SwayingWheatfield/OLMo-32B-Spatial-Thinking-Preview# Run inference directly in the terminal:
./llama-cli -hf SwayingWheatfield/OLMo-32B-Spatial-Thinking-PreviewBuild from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf SwayingWheatfield/OLMo-32B-Spatial-Thinking-Preview# Run inference directly in the terminal:
./build/bin/llama-cli -hf SwayingWheatfield/OLMo-32B-Spatial-Thinking-PreviewUse Docker
docker model run hf.co/SwayingWheatfield/OLMo-32B-Spatial-Thinking-Preview🚀 Askit-OLMo-32B-Spatial-Thinking-Preview
AI-Powered Physics Simulation & Mathematical Animation Generation
Explicit Spatial Reasoning + API-Centric Code Generation
✨ What Makes Askit Special?
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.
🧠 Core Innovation: Forced Spatial Cognition
Unlike traditional models that generate code directly, Askit enforces a complete spatial thinking pipeline:
Physics Problem
↓
【空间直觉分析】 (3D Space Analysis)
↓
【空间坐标计算】 (Coordinate Calculations)
↓
【物理→API翻译】 (Physics → API Translation)
↓
Production-Ready 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
🎯 Capabilities
1. Spatial Understanding 🗺️
- 3D object position relationships
- Precise coordinate calculations (x, y, z)
- Automatic trajectory derivation
- Explicit spatial reasoning in every response
2. Physics Modeling ⚙️
- Classical mechanics, electromagnetism, thermodynamics
- API-Driven: Direct PhysicsBridge API generation
- Rigid body collisions, SPH fluids, ODE/PDE solvers
- Complex multi-body systems with constraints
3. Advanced Problem Solving 🏆
- CPhO (Chinese Physics Olympiad) level optimization
- IMO (International Mathematical Olympiad) level optimization
- Deep reasoning for competition problems
- Production-ready simulation code
🛠️ Ecosystem & Integration
📱 Official Software
Askit. - Interactive Physics Animation Platform
- 🌐 Website: askit.space
- 💻 GitHub: github.com/SStarrySSky/Askit.
- 📦 Features:
- Real-time physics simulation with OpenGL rendering
- Interactive controls (sliders, buttons, checkboxes)
- Timeline-based animation control
- Snapshot system for AI context awareness
- PhysicsBridge API integration
🔗 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>
【空间直觉分析】
- 3D space structure analysis
- Initial positions (x₀, y₀, z₀)
- Initial velocities (vₓ, vᵧ, vᵤ)
- Coordinate system setup
【物理原理推导】
- Applicable physics laws
- Force analysis
- Acceleration calculations
【空间坐标计算】
- Position at time t: (x(t), y(t), z(t))
- Velocity vector: (vₓ(t), vᵧ(t), vᵤ(t))
- Trajectory equations
【物理→API翻译】
- 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
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
- 🌐 Website: askit.space
- 💻 GitHub Repository: github.com/SStarrySSky/Askit.
- 🤗 HuggingFace Model: SStarrySSky/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
- Downloads last month
- 22
Evaluation results
- accuracy on CPhO & IMO Level Problemsself-reportedHigh spatial reasoning accuracy
Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf SwayingWheatfield/OLMo-32B-Spatial-Thinking-Preview# Run inference directly in the terminal: llama-cli -hf SwayingWheatfield/OLMo-32B-Spatial-Thinking-Preview