DeepSeek R1 Distill Qwen 1.5B
Reasoning. Laptop-sized.
Compact Power. Serious Reasoning.
Distilled Intelligence
671B Reasoning Compressed
Knowledge distilled from massive DeepSeek-R1 into 1.5B parameters without performance loss.
Hardware Efficient
4GB GPU Memory
Runs on single laptop GPU in 8-bit quantization for local deployment and edge inference.
Chain-of-Thought
Math and Code Mastery
Excels at step-by-step problem solving, mathematical reasoning, and code comprehension tasks.
Examples
See what DeepSeek R1 Distill Qwen 1.5B can create
Copy any prompt below and try it yourself in the playground.
Calculus Problem
“Solve this step-by-step: Find the derivative of f(x) = 3x^4 - 2x^2 + 5x - 1 and evaluate at x = 2. Show all work.”
Algorithm Analysis
“Explain the time complexity of a binary search tree insertion operation. Compare it to a linear search approach with code examples.”
Logic Puzzle
“Five people sit in a row. Alice is not next to Bob. Charlie sits between Diana and Eve. Who sits where? Work through the constraints.”
Code Debugging
“Debug this Python function that should return the sum of even numbers in a list: def sum_evens(nums): total = 0; for n in nums: if n % 2 == 0: total += n; return total. Identify issues.”
For Developers
A few lines of code.
Reasoning. Four gigabytes.
ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.
- Serverless: scales to zero, scales to millions
- Pay per token, no minimums
- Python and JavaScript SDKs, plus REST API
import requestsresponse = requests.post("https://modelslab.com/api/v7/llm/chat/completions",json={"key": "YOUR_API_KEY","prompt": "","model_id": ""})print(response.json())
Ready to create?
Start generating with DeepSeek R1 Distill Qwen 1.5B on ModelsLab.